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3DLinker - An E(3) Equivariant Variational Autoencoder | 2,022 | ICML | Oral | 4 | null | 3 | Yes | null | 45,879 | 19 | Molecular Linker Design;E(3) Equivariant Graph Variational Autoencoder;Conditional Generative Models;3D Molecular Structure Prediction | https://arxiv.org/pdf/2205.07309 | https://icml.cc/media/icml-2022/Slides/18143.pdf |
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness | 2,022 | ICML | Oral | 4 | null | 3 | Yes | null | 44,347 | 47 | Counterfactual Inference;Data Integration;Fair Representation Learning;Batch Effect Correction | https://arxiv.org/pdf/2106.08161 | https://icml.cc/media/icml-2022/Slides/17234.pdf |
Correct-N-Contrast - A Contrastive Approach for Improving Robustness to Spurious Correlations | 2,022 | ICML | Oral | 7 | null | 4 | Yes | null | 44,964 | 78 | Robust Machine Learning;Spurious Correlations;Contrastive Learning;Representation Alignment | https://arxiv.org/pdf/2203.01517 | https://icml.cc/media/icml-2022/Slides/18224_Wlco9OF.pdf |
Learning inverse folding from millions of predicted structures | 2,022 | ICML | Oral | 7 | null | 3 | Yes | null | 42,125 | 18 | Inverse Folding Prediction;Protein Structure Prediction;Generative Models for Protein Design;Sequence-to-Sequence Learning in Protein Engineering | https://proceedings.mlr.press/v162/hsu22a/hsu22a.pdf | https://icml.cc/media/icml-2022/Slides/16886.pdf |
Monarch - Expressive Structured Matrices for Efficient and Accurate Training | 2,022 | ICML | Oral | 5 | null | 8 | Yes | null | 43,885 | 44 | Structured Matrices;Sparse Training;Efficient Neural Network Training;Matrix Approximation Techniques | https://arxiv.org/pdf/2204.00595 | https://icml.cc/media/icml-2022/Slides/17900_R7TNeV4.pdf |
POEM - Out-of-Distribution Detection with Posterior Sampling | 2,022 | ICML | Oral | 3 | null | 3 | Yes | null | 54,444 | 34 | Out-of-Distribution Detection;Posterior Sampling;Outlier Detection;Neural Network Regularization | https://arxiv.org/pdf/2206.13687 | https://icml.cc/media/icml-2022/Slides/16652.pdf |
Path-Gradient Estimators for Continuous Normalizing Flows | 2,022 | ICML | Oral | 5 | null | 3 | Yes | null | 39,565 | 29 | Path-Gradient Estimators;Continuous Normalizing Flows;Variational Inference;High-Dimensional Systems | https://arxiv.org/pdf/2206.09016 | https://icml.cc/media/icml-2022/Slides/17304_0S8DqlX.pdf |
Privacy for Free - How does Dataset Condensation Help Privacy | 2,022 | ICML | Oral | 8 | null | 3 | Yes | null | 53,800 | 24 | Dataset Condensation;Differential Privacy;Membership Inference Attacks;Data Privacy in Machine Learning | https://arxiv.org/pdf/2206.00240 | https://icml.cc/media/icml-2022/Slides/18236.pdf |
Rethinking Image-Scaling Attacks | 2,022 | ICML | Oral | 9 | null | 4 | Yes | null | 47,649 | 44 | Image Scaling Algorithms;Adversarial Attacks;Machine Learning Vulnerabilities;Decision-Based Black-Box Attacks | https://arxiv.org/pdf/2104.08690 | https://icml.cc/media/icml-2022/Slides/16968_QVuMEKF.pdf |
RieszNet and ForestRiesz - Automatic Debiased | 2,022 | ICML | Oral | 3 | null | 4 | Yes | null | 46,414 | 33 | Automatic Debiasing;Riesz Representation;Neural Networks;Random Forests | https://arxiv.org/pdf/2110.03031 | https://icml.cc/media/icml-2022/Slides/16312_f0zLRYT.pdf |
To Smooth or Not When Label Smoothing Meets Noisy Labels | 2,022 | ICML | Oral | 5 | null | 6 | Yes | null | 46,876 | 27 | Label Smoothing;Noisy Label Learning;Regularization Techniques;Negative Label Smoothing | https://arxiv.org/pdf/2106.04149 | https://icml.cc/media/icml-2022/Slides/17074.pdf |
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning | 2,022 | ICML | Oral | 8 | null | 4 | No | null | 51,214 | 20 | Topology-Aware Network Pruning;Graph Neural Networks;Reinforcement Learning;Model Compression | https://arxiv.org/pdf/2102.03214 | https://icml.cc/media/icml-2022/Slides/16772.pdf |
Understanding Dataset Difficulty with V-Usable Information | 2,022 | ICML | Oral | 9 | null | 4 | Yes | null | 58,804 | 32 | Dataset Difficulty Estimation;V-Usable Information;Pointwise V-Information;NLP Benchmark Analysis | https://arxiv.org/pdf/2110.08420 | https://icml.cc/media/icml-2022/Slides/16634.pdf |
Unified Scaling Laws for Routed Language Models | 2,022 | ICML | Oral | 13 | null | 6 | Yes | null | 45,020 | 84 | Scaling Laws in Language Models;Routing Networks;Neural Network Architecture Evaluation;Effective Parameter Count | https://arxiv.org/pdf/2202.01169 | https://icml.cc/media/icml-2022/Slides/17820.pdf |
data2vec - A General Framework for Self-supervised Learning in Speech, Vision and Language | 2,022 | ICML | Oral | 3 | null | 6 | Yes | null | 47,928 | 14 | Self-supervised Learning;Multimodal Learning;Contextualized Representations;Speech Recognition | https://arxiv.org/pdf/2202.03555 | https://icml.cc/media/icml-2022/Slides/16644.pdf |
Adversarial Example Does Good - Preventing Painting Imitation from | 2,023 | ICML | Oral | 5 | null | 4 | Yes | null | 43,771 | 12 | Adversarial Training;Diffusion Models;Copyright Protection in AI Art;Image Synthesis | https://arxiv.org/pdf/2302.04578 | https://icml.cc/media/icml-2023/Slides/25469.pdf |
Audio Pre-Training with Acoustic Tokenizers | 2,023 | ICML | Oral | 4 | null | 3 | Yes | null | 49,406 | 28 | Self-Supervised Learning for Audio;Acoustic Tokenization;Audio Representation Learning;Audio Classification Benchmarking | https://arxiv.org/pdf/2212.09058 | https://icml.cc/media/icml-2023/Slides/25555.pdf |
Bidirectional Adaptation for Robust Semi-Supervised Learning | 2,023 | ICML | Oral | 3 | null | 3 | Yes | null | 52,501 | 8 | Robust Semi-Supervised Learning;Distribution Adaptation;Debiased Pseudo-Labeling;Theoretical Framework for SSL | https://proceedings.mlr.press/v202/jia23a/jia23a.pdf | https://icml.cc/media/icml-2023/Slides/25533.pdf |
Evaluating Self-Supervised Learning via Risk Decomposition | 2,023 | ICML | Oral | 13 | null | 4 | Yes | null | 58,816 | 28 | Self-Supervised Learning;Risk Decomposition;Error Analysis in Representation Learning;Evaluation Metrics for SSL Models | https://arxiv.org/pdf/2302.03068 | https://icml.cc/media/icml-2023/Slides/25480_fMGoWdI.pdf |
Fast Inference from Transformers via Speculative Decoding | 2,023 | ICML | Oral | 5 | null | 3 | Yes | null | 34,452 | 14 | Speculative Decoding;Parallel Inference;Autoregressive Models | https://arxiv.org/pdf/2211.17192 | https://icml.cc/media/icml-2023/Slides/25546_yAOj5U8.pdf |
Instant Soup - Cheap Pruning Ensembles in A Single Pass Can | 2,023 | ICML | Oral | 3 | null | 7 | Yes | null | 46,357 | 8 | Pruning Ensembles;Lottery Ticket Hypothesis;Model Fine-Tuning;Large Pre-trained Transformers | https://arxiv.org/pdf/2306.10460 | https://icml.cc/media/icml-2023/Slides/25544.pdf |
ODS - Test-Time Adaptation in the Presence of Open-World Data Shift | 2,023 | ICML | Oral | 7 | null | 7 | Yes | null | 43,543 | 21 | Test-Time Adaptation;Open-World Data Shift;Covariate and Label Distribution Shift | https://proceedings.mlr.press/v202/zhou23e/zhou23e.pdf | https://icml.cc/media/icml-2023/Slides/25549.pdf |
Pre-training for Speech Translation - CTC Meets Optimal Transport | 2,023 | ICML | Oral | 5 | null | 10 | Yes | null | 48,994 | 21 | Speech-to-Text Translation;Connectionist Temporal Classification;Optimal Transport;Siamese Neural Networks | https://arxiv.org/pdf/2301.11716 | https://icml.cc/media/icml-2023/Slides/25497.pdf |
Refining Generative Process with Discriminator Guidance | 2,023 | ICML | Oral | 15 | null | 8 | Yes | null | 45,867 | 19 | Score-based Diffusion Models;Discriminator-guided Learning;Generative Adversarial Networks (GANs) Alternative Approaches;Image Generation Techniques | https://arxiv.org/pdf/2211.17091 | https://icml.cc/media/icml-2023/Slides/25468.pdf |
Subequivariant Graph Reinforcement Learning in 3D Environments | 2,023 | ICML | Oral | 8 | null | 5 | Yes | null | 47,869 | 21 | Morphology-Agnostic Reinforcement Learning;Subequivariant Graph Neural Networks;3D Environment Exploration;Policy Optimization via Geometric Symmetry | https://arxiv.org/pdf/2305.18951 | https://icml.cc/media/icml-2023/Slides/25559.pdf |
Which Features are Learnt by Contrastive Learning | 2,023 | ICML | Oral | 6 | null | 5 | Yes | null | 54,697 | 62 | Contrastive Learning;Representation Learning;Feature Suppression;Class Collapse | https://arxiv.org/pdf/2305.16536 | https://icml.cc/media/icml-2023/Slides/25437_64AmEtv.pdf |
Causal normalizing flows - from theory to practice | 2,023 | NeurIPS | Oral | 5 | null | 3 | Yes | null | 52,327 | 67 | Causal Inference;Normalizing Flows;Autoregressive Models;Counterfactual Reasoning | https://arxiv.org/pdf/2306.05415 | https://neurips.cc/media/neurips-2023/Slides/73851.pdf |
Conformal Meta-learners for Predictive Inference of | 2,023 | NeurIPS | Oral | 5 | null | 3 | Yes | null | 53,153 | 25 | Individual Treatment Effects;Conformal Prediction;Meta-Learning;Causal Inference | https://arxiv.org/pdf/2308.14895 | https://neurips.cc/media/neurips-2023/Slides/72090.pdf |
Learning Linear Causal Representations from Interventions | 2,023 | NeurIPS | Oral | 3 | null | 3 | Yes | null | 62,186 | 31 | Causal Inference;Identification of Latent Variables;Contrastive Learning;High-Dimensional Geometry in Causal Learning | https://arxiv.org/pdf/2306.02235 | https://neurips.cc/media/neurips-2023/Slides/73823.pdf |
QL ORA - Efficient Finetuning of Quantized LLMs | 2,023 | NeurIPS | Oral | 3 | null | 8 | Yes | null | 75,353 | 20 | Efficient Finetuning of Quantized Language Models;Low Rank Adaptation (LoRA);Memory Optimization Techniques;Chatbot Performance Evaluation | https://arxiv.org/pdf/2305.14314 | https://neurips.cc/media/neurips-2023/Slides/73855.pdf |
Learning to Segment Referred Objects from Narrated Egocentric Videos | 2,024 | CVPR | Oral | 4 | null | 3 | No | null | 55,536 | 17 | Weakly-Supervised Video Object Segmentation;Vision-Language Models;Contrastive Learning;Egocentric Video Analysis | https://openaccess.thecvf.com/content/CVPR2024/papers/Shen_Learning_to_Segment_Referred_Objects_from_Narrated_Egocentric_Videos_CVPR_2024_paper.pdf | https://cvpr.thecvf.com/media/cvpr-2024/Slides/29467.pdf |
CAMERAS AS RAYS - POSE ESTIMATION VIA RAY DIFFUSION | 2,024 | ICLR | Oral | 7 | null | 3 | Yes | null | 40,716 | 21 | Pose Estimation;Ray Diffusion;3D Reconstruction;Denoising Diffusion Models | https://arxiv.org/pdf/2402.14817 | https://iclr.cc/media/iclr-2024/Slides/19778.pdf |
CLIM ODE - C LIMATE AND WEATHER FORECASTING | 2,024 | ICLR | Oral | 8 | null | 3 | Yes | null | 35,588 | 35 | Physics-Informed Neural Networks;Weather Forecasting;Uncertainty Quantification;Spatiotemporal Modeling | https://arxiv.org/pdf/2404.10024 | https://iclr.cc/media/iclr-2024/Slides/19715.pdf |
LESS IS MORE - F EWER INTERPRETABLE REGION VIA | 2,024 | ICLR | Oral | 3 | null | 4 | Yes | null | 47,332 | 18 | Image Attribution;Submodular Optimization;Model Interpretability | https://arxiv.org/pdf/2402.09164 | https://iclr.cc/media/iclr-2024/Slides/19733.pdf |
METAGPT - M ETA PROGRAMMING FOR A MULTI-AGENT COLLABORATIVE FRAMEWORK | 2,024 | ICLR | Oral | 5 | null | 6 | Yes | null | 46,728 | 17 | Meta-Programming;Multi-Agent Systems;Large Language Models;Automated Problem Solving | https://arxiv.org/pdf/2308.00352 | https://iclr.cc/media/iclr-2024/Slides/18491.pdf |
A Touch, Vision, and Language Dataset for Multimodal Alignment | 2,024 | ICML | Oral | 6 | null | 4 | Yes | null | 42,012 | 48 | Multimodal Representation Learning;Tactile Data Annotation;Vision-Touch Language Alignment;Generative Language Models | https://arxiv.org/pdf/2402.13232 | https://icml.cc/media/icml-2024/Slides/35452.pdf |
APT - Adaptive Pruning and Tuning Pretrained Language Models for | 2,024 | ICML | Oral | 3 | null | 7 | Yes | null | 48,883 | 20 | Adaptive Pruning;Efficient Fine-Tuning;Parameter-Efficient Models;Language Model Optimization | https://arxiv.org/pdf/2401.12200 | https://icml.cc/media/icml-2024/Slides/35453.pdf |
Arrows_of_Time_for_Large_Language_Models | 2,024 | ICML | Oral | 4 | null | 5 | Yes | null | 52,767 | 85 | Autoregressive Language Modeling;Time Asymmetry in Probabilistic Models;Information Theory in AI Systems;Sparse Representation in Neural Networks | https://arxiv.org/pdf/2401.17505 | https://icml.cc/media/icml-2024/Slides/35521.pdf |
Bottleneck-Minimal Indexing for Generative Document Retrieval | 2,024 | ICML | Oral | 6 | null | 4 | Yes | null | 45,364 | 24 | Generative Document Retrieval;Information-Theoretic Approaches;Rate-Distortion Theory;Neural Autoregressive Models | https://arxiv.org/pdf/2405.10974 | https://icml.cc/media/icml-2024/Slides/35532.pdf |
Candidate Pseudolabel Learning - Enhancing Vision-Language Models by | 2,024 | ICML | Oral | 7 | null | 6 | Yes | null | 48,882 | 15 | Candidate Pseudolabel Learning;Vision-Language Models;Unlabeled Data Fine-tuning;Prompt Tuning | https://arxiv.org/pdf/2406.10502 | https://icml.cc/media/icml-2024/Slides/35456.pdf |
Challenges in Training PINNs - A Loss Landscape Perspective | 2,024 | ICML | Oral | 8 | null | 3 | Yes | null | 53,420 | 30 | Physics-Informed Neural Networks;Loss Landscape Optimization;Gradient-Based Optimization Methods;Partial Differential Equations | https://arxiv.org/pdf/2402.01868 | https://icml.cc/media/icml-2024/Slides/33180_kLYAzFO.pdf |
Data-free Neural Representation Compression | 2,024 | ICML | Oral | 3 | null | 5 | Yes | null | 47,302 | 18 | Riemannian Geometry in Neural Networks;Data-free Neural Network Compression;Dynamic Systems and Neural Representation;Neuronal Interaction Modeling | https://raw.githubusercontent.com/mlresearch/v235/main/assets/pei24d/pei24d.pdf | https://icml.cc/media/icml-2024/Slides/35536_gVhPtee.pdf |
ExCP - Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking | 2,024 | ICML | Oral | 5 | null | 7 | Yes | null | 42,889 | 14 | Checkpoint Compression;Weight-Momentum Optimization;Non-Uniform Quantization;Sparse Information Extraction | https://arxiv.org/pdf/2406.11257 | https://icml.cc/media/icml-2024/Slides/35484_oOBhtPf.pdf |
Expressivity and Generalization - Fragment-Biases for Molecular GNNs | 2,024 | ICML | Oral | 6 | null | 7 | Yes | null | 50,175 | 23 | Fragment-Biased Graph Neural Networks;Theoretic Expressivity Analysis;Molecular Property Prediction;Generalization in Machine Learning | https://arxiv.org/pdf/2406.08210 | https://icml.cc/media/icml-2024/Slides/35459.pdf |
Listenable Maps for Zero-Shot Audio Classifiers | 2,024 | ICML | Oral | 5 | null | 3 | Yes | null | 45,423 | 42 | Explainable AI;Zero-Shot Learning;Audio Classification;Post-Hoc Explanation Methods | https://arxiv.org/pdf/2405.17615 | https://icml.cc/media/icml-2024/Slides/33268.pdf |
MLLM-as-a-Judge - Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark | 2,024 | ICML | Oral | 9 | null | 8 | Yes | null | 61,182 | 33 | Multimodal Large Language Models;Benchmark Development;Judgment Biases in AI;Evaluation Metrics for AI Systems | https://arxiv.org/pdf/2402.04788 | https://icml.cc/media/icml-2024/Slides/35497.pdf |
MorphGrower - A Synchronized Layer-by-layer Growing Approach for | 2,024 | ICML | Oral | 5 | null | 3 | Yes | null | 49,482 | 19 | Neuronal Morphology Generation;Synchronized Layer-by-layer Growth;Topological Validity in Morphologies;Electrophysiological Response Simulation | https://arxiv.org/pdf/2401.09500 | https://icml.cc/media/icml-2024/Slides/35513.pdf |
Position - Rethinking Post-Hoc Search-Based Neural Approaches for Solving | 2,024 | ICML | Oral | 4 | null | 3 | Yes | null | 45,092 | 25 | Heatmap Generation for Optimization;Monte Carlo Tree Search;Traveling Salesman Problem;Combinatorial Optimization Methods | https://arxiv.org/pdf/2406.03503 | https://icml.cc/media/icml-2024/Slides/35505.pdf |
SparseTSF - Modeling Long-term Time Series Forecasting with 1k Parameters | 2,024 | ICML | Oral | 4 | null | 7 | Yes | null | 49,044 | 18 | Long-term Time Series Forecasting;Cross-Period Sparse Forecasting;Parameter Efficiency in Machine Learning Models;Generalization in Low-Resource Scenarios | https://arxiv.org/pdf/2405.00946 | https://icml.cc/media/icml-2024/Slides/35571.pdf |
Towards_Optimal_Adversarial_Robust_Q-learning_with_Bellman_Infinity-error | 2,024 | ICML | Oral | 5 | null | 3 | Yes | null | 54,693 | 30 | Adversarial Robustness in Reinforcement Learning;Optimal Robust Policy in Markov Decision Processes;Bellman Error Minimization Techniques;Deep Q-Networks Training Methods | https://arxiv.org/pdf/2402.02165 | https://icml.cc/media/icml-2024/Slides/33033_8TELIdY.pdf |
Unified_Training_of_Universal_Time_Series_Forecasting_Transformers | 2,024 | ICML | Oral | 6 | null | 7 | Yes | null | 57,564 | 39 | Universal Time Series Forecasting;Transformer Architectures;Cross-Frequency Learning;Multivariate Time Series Analysis | https://arxiv.org/pdf/2402.02592 | https://icml.cc/media/icml-2024/Slides/35515.pdf |
Video-of-Thought - Step-by-Step Video Reasoning from Perception to Cognition | 2,024 | ICML | Oral | 8 | null | 4 | Yes | null | 59,901 | 27 | Video Understanding;Spatial-Temporal Reasoning;Multimodal Large Language Models;Cognitive Video Comprehension | https://arxiv.org/pdf/2501.03230 | https://icml.cc/media/icml-2024/Slides/33467.pdf |
AgentBoard - An Analytical Evaluation Board of | 2,024 | NeurIPS | Oral | 6 | null | 7 | Yes | null | 59,530 | 62 | Evaluation Frameworks for Large Language Models;Multi-Turn Interaction in AI Agents;Benchmarking of Agent Performance;Interpretable AI | https://arxiv.org/pdf/2401.13178 | https://neurips.cc/media/neurips-2024/Slides/97853.pdf |
DapperFL - Domain Adaptive Federated Learning with | 2,024 | NeurIPS | Oral | 4 | null | 3 | Yes | null | 52,291 | 29 | Federated Learning;Domain Adaptation;Model Pruning;Edge Computing | https://arxiv.org/pdf/2412.05823 | https://neurips.cc/media/neurips-2024/Slides/95295.pdf |
Decompose, Analyze and Rethink | 2,024 | NeurIPS | Oral | 5 | null | 4 | Yes | null | 53,419 | 12 | Natural Language Processing;Reasoning in AI;Tree-based Question Decomposition;Large Language Models | https://proceedings.neurips.cc/paper_files/paper/2024/file/01025a4e79355bb37a10ba39605944b5-Paper-Conference.pdf | https://neurips.cc/media/neurips-2024/Slides/97984.pdf |
You Only Cache Once - Decoder-Decoder Architectures for Language Models | 2,024 | NeurIPS | Oral | 10 | null | 5 | Yes | null | 44,856 | 12 | Decoder-Decoder Architectures;Key-Value Caching Optimization;Transformer Model Efficiency;Global Attention Mechanisms | https://arxiv.org/pdf/2405.05254 | https://neurips.cc/media/neurips-2024/Slides/98001.pdf |
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning | 2,025 | CVPR | Oral | 8 | null | 7 | No | null | 51,814 | 10 | Federated Learning;Data Heterogeneity;Geometric Distribution Alignment;Sample Generation Techniques | https://arxiv.org/pdf/2503.06457 | https://cvpr.thecvf.com/media/cvpr-2025/Slides/32823.pdf |
Deterministic Object Pose Confidence Region Estimation | 2,025 | ICCV | Oral | 6 | null | 6 | No | null | 44,266 | 27 | 6D Pose Estimation;Uncertainty Quantification;Deterministic Estimation Methods;Conformal Prediction | https://arxiv.org/pdf/2506.22720 | https://iccv.thecvf.com/media/iccv-2025/Slides/2258_KrNKhsn.pdf |
Diffusion Image Prior | 2,025 | ICCV | Oral | 7 | null | 6 | No | null | 35,462 | 22 | Diffusion Models;Image Restoration;Blind Image Restoration;Artifact Removal | https://arxiv.org/pdf/2503.21410 | https://iccv.thecvf.com/media/iccv-2025/Slides/2934.pdf |
Diving into the Fusion of Monocular Priors for Generalized Stereo Matching | 2,025 | ICCV | Oral | 4 | null | 7 | No | null | 48,476 | 35 | Stereo Matching;Monocular Depth Estimation;Depth Map Fusion;Ill-posed Problem Handling | https://arxiv.org/pdf/2505.14414 | https://iccv.thecvf.com/media/iccv-2025/Slides/2920_HA4wPU8.pdf |
Learning Streaming Video Representation via Multitask Training | 2,025 | ICCV | Oral | 3 | null | 6 | No | null | 62,845 | 19 | Streaming Video Representation;Multitask Learning;Online Action Detection;Video Question Answering | https://arxiv.org/pdf/2504.20041 | https://iccv.thecvf.com/media/iccv-2025/Slides/2901.pdf |
ACCELERATED TRAINING THROUGH ITERATIVE | 2,025 | ICLR | Oral | 6 | null | 4 | Yes | null | 44,150 | 14 | Highway Backpropagation;Residual Networks;Gradient Propagation Algorithms;Deep Learning Optimization | https://arxiv.org/pdf/2501.17086 | https://iclr.cc/media/iclr-2025/Slides/31872.pdf |
BOOSTER - T ACKLING HARMFUL FINE -TUNING FOR | 2,025 | ICLR | Oral | 3 | null | 9 | Yes | null | 61,851 | 16 | Harmful Fine-Tuning Attacks;Large Language Model Alignment;Loss Regularization Techniques | https://arxiv.org/pdf/2409.01586 | https://iclr.cc/media/iclr-2025/Slides/28050.pdf |
CHART MOE - M IXTURE OF DIVERSELY ALIGNED EX | 2,025 | ICLR | Oral | 11 | null | 8 | Yes | null | 58,200 | 13 | Chart Understanding;Mixture of Experts (MoE);Multimodal Large Language Models (MLLMs);Dataset Creation for Chart Analysis | https://arxiv.org/pdf/2409.03277 | https://iclr.cc/media/iclr-2025/Slides/31773.pdf |
CYBER HOST - A O NE-STAGE DIFFUSION FRAMEWORK | 2,025 | ICLR | Oral | 6 | null | 3 | Yes | null | 49,641 | 21 | Audio-Driven Talking Body Generation;Diffusion-Based Video Generation;Human Animation Synthesis;Region Attention Module in Animation | https://arxiv.org/pdf/2409.01876 | https://iclr.cc/media/iclr-2025/Slides/32123.pdf |
DEPT - D ECOUPLED EMBEDDINGS FOR PRE | 2,025 | ICLR | Oral | 5 | null | 13 | Yes | null | 67,319 | 23 | Decoupled Embeddings;Federated Learning;Language Model Pre-training;Communication-Efficient Training | https://arxiv.org/pdf/2410.05021 | https://iclr.cc/media/iclr-2025/Slides/27901_qTj5Ifm.pdf |
FLAT REWARD IN POLICY PARAMETER SPACE IMPLIES | 2,025 | ICLR | Oral | 7 | null | 3 | Yes | null | 48,254 | 30 | Flat Reward Landscapes;Robustness in Reinforcement Learning;Policy Parameter Space Analysis;Generalization in Deep Neural Networks | https://proceedings.iclr.cc/paper_files/paper/2025/file/3448058d44f133839042295f79a9a958-Paper-Conference.pdf | https://iclr.cc/media/iclr-2025/Slides/31923.pdf |
GESUBNET - G ENE INTERACTION INFERENCE FOR | 2,025 | ICLR | Oral | 6 | null | 4 | Yes | null | 54,038 | 21 | Gene Interaction Inference;Disease Subtype Network Generation;Graph Neural Networks;Representation Learning | https://arxiv.org/pdf/2410.13178 | https://iclr.cc/media/iclr-2025/Slides/28630_yPoleh1.pdf |
GRID MIX - E XPLORING SPATIAL MODULATION FOR | 2,025 | ICLR | Oral | 4 | null | 6 | Yes | null | 50,700 | 20 | Spatial Modulation;Neural Fields;Partial Differential Equations (PDE) Modeling;Domain Augmentation | https://openreview.net/pdf?id=Fur0DtynPX | https://iclr.cc/media/iclr-2025/Slides/31883.pdf |
IMPROVING PROBABILISTIC DIFFUSION MODELS WITH | 2,025 | ICLR | Oral | 4 | null | 8 | Yes | null | 51,204 | 43 | Probabilistic Diffusion Models;Optimal Covariance Matching;Denoising Distribution;Sampling Efficiency | https://arxiv.org/pdf/2406.10808 | https://iclr.cc/media/iclr-2025/Slides/28877.pdf |
IMPROVING PROBABILISTIC DIFFUSION MODELS WITH (2) | 2,025 | ICLR | Oral | 4 | null | 8 | Yes | null | 51,204 | 17 | Probabilistic Diffusion Models;Diagonal Covariance Matching;Optimal Covariance Matching;Sampling Efficiency | https://arxiv.org/pdf/2410.13708 | https://iclr.cc/media/iclr-2025/Slides/28788.pdf |
Inference_Scaling_for_Long-Context_Retrieval_Augmented_Generation | 2,025 | ICLR | Oral | 8 | null | 4 | Yes | null | 46,464 | 38 | Long-Context Large Language Models;Retrieval Augmented Generation;Inference Scaling Laws;Optimal Computation Allocation | https://arxiv.org/pdf/2410.04343 | https://iclr.cc/media/iclr-2025/Slides/30339.pdf |
KNOWING YOUR TARGET - TARGET -AWARE TRANSFORMER MAKES BETTER SPATIO-T EMPORAL | 2,025 | ICLR | Oral | 6 | null | 10 | Yes | null | 52,853 | 17 | Spatio-Temporal Video Grounding;Target-Aware Transformers;Multimodal Feature Interactions;Object Query Initialization | https://arxiv.org/pdf/2502.11168 | https://iclr.cc/media/iclr-2025/Slides/29364.pdf |
LEARNING TO DISCRETIZE | 2,025 | ICLR | Oral | 6 | null | 8 | Yes | null | 49,108 | 29 | Diffusion Probabilistic Models;Sampling Efficiency;Neural Function Evaluations;Generative Model Optimization | https://arxiv.org/pdf/2405.15506 | https://iclr.cc/media/iclr-2025/Slides/31735.pdf |
MEASURING AND ENHANCING TRUSTWORTHINESS OF | 2,025 | ICLR | Oral | 3 | null | 10 | Yes | null | 65,340 | 39 | Trustworthiness Evaluation in LLMs;Retrieval-Augmented Generation (RAG);Prompting Methods for LLMs;Model Alignment Techniques | https://arxiv.org/pdf/2409.11242 | https://iclr.cc/media/iclr-2025/Slides/31873.pdf |
NEURAL PLANE - S TRUCTURED 3D R ECONSTRUCTION | 2,025 | ICLR | Oral | 7 | null | 3 | Yes | null | 48,329 | 16 | 3D Plane Reconstruction;Neural Fields;Self-Supervised Learning;Semantic Segmentation | https://openreview.net/pdf?id=5UKrnKuspb | https://iclr.cc/media/iclr-2025/Slides/30944.pdf |
Navigating the Digital World as Humans Do | 2,025 | ICLR | Oral | 5 | null | 9 | Yes | null | 61,854 | 26 | Visual Grounding;Multimodal Learning;Graphical User Interface (GUI) Agents;Synthetic Data Generation | https://arxiv.org/pdf/2410.05243 | https://iclr.cc/media/iclr-2025/Slides/32124.pdf |
OPEN -VOCABULARY OBJECT DETECTION VIA | 2,025 | ICLR | Oral | 7 | null | 6 | Yes | null | 48,188 | 12 | Open-Vocabulary Object Detection;Knowledge Distillation;Vision and Language Integration;Transfer Learning | https://arxiv.org/pdf/2104.13921 | https://iclr.cc/media/iclr-2025/Slides/31930.pdf |
Open-YOLO 3D - Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation | 2,025 | ICLR | Oral | 4 | null | 5 | Yes | null | 38,184 | 17 | Open-Vocabulary 3D Instance Segmentation;2D Object Detection;Multi-View Image Processing;Real-Time Inference Techniques | https://arxiv.org/pdf/2406.02548 | https://iclr.cc/media/iclr-2025/Slides/31900_8PUpb67.pdf |
PROBABILISTIC LEARNING TO DEFER - H ANDLING | 2,025 | ICLR | Oral | 5 | null | 3 | Yes | null | 47,151 | 47 | Learning to Defer;Probabilistic Modelling;Workload Distribution in Human-AI Cooperation;Handling Missing Annotations | https://openreview.net/pdf?id=zl0HLZOJC9 | https://iclr.cc/media/iclr-2025/Slides/31728.pdf |
REPRESENTATION ALIGNMENT FOR GENERATION | 2,025 | ICLR | Oral | 8 | null | 5 | Yes | null | 61,444 | 19 | Representation Alignment;Denoising Diffusion Models;Visual Representation Learning;Training Efficiency in Generative Models | https://arxiv.org/pdf/2410.06940 | https://iclr.cc/media/iclr-2025/Slides/31896.pdf |
SD-L ORA - S CALABLE DECOUPLED LOW-RANK ADAP | 2,025 | ICLR | Oral | 5 | null | 5 | Yes | null | 46,379 | 28 | Class Incremental Learning;Low-Rank Adaptation;Scalable Machine Learning;Continual Learning | https://arxiv.org/pdf/2501.13198 | https://iclr.cc/media/iclr-2025/Slides/31918.pdf |
SPIDER 2.0 - E VALUATING LANGUAGE MODELS ON | 2,025 | ICLR | Oral | 18 | null | 18 | Yes | null | 58,210 | 23 | Text-to-SQL Transformation;SQL Query Generation;Model Evaluation Frameworks;Enterprise Database Systems | https://arxiv.org/pdf/2411.07763 | https://iclr.cc/media/iclr-2025/Slides/31826.pdf |
STANDARD GAUSSIAN PROCESS IS ALL YOU NEED FOR | 2,025 | ICLR | Oral | 5 | null | 4 | Yes | null | 44,476 | 24 | Bayesian Optimization;Gaussian Processes;Matérn Kernels;High-Dimensional Optimization | https://arxiv.org/pdf/2402.02746 | https://iclr.cc/media/iclr-2025/Slides/31784.pdf |
TIME MIXER ++ - A G ENERAL TIME SERIES PATTERN | 2,025 | ICLR | Oral | 5 | null | 10 | Yes | null | 59,545 | 18 | Time Series Analysis;Pattern Extraction;Multi-Resolution Time Imaging;Anomaly Detection | https://arxiv.org/pdf/2410.16032 | https://iclr.cc/media/iclr-2025/Slides/31932.pdf |
TOWARD GUIDANCE -F REE AR V ISUAL GENERATION | 2,025 | ICLR | Oral | 8 | null | 3 | Yes | null | 60,033 | 26 | Classifier-Free Guidance;Autoregressive Visual Generation;Contrastive Learning;Multi-Modal Alignment | https://arxiv.org/pdf/2410.09347 | https://iclr.cc/media/iclr-2025/Slides/31786.pdf |
UNLOCKING STATE-TRACKING IN LINEAR RNN S | 2,025 | ICLR | Oral | 9 | null | 5 | Yes | null | 56,524 | 22 | Linear Recurrent Neural Networks;State-Tracking in Neural Networks;Eigenvalue Analysis in Neural Networks;Language Modeling | https://arxiv.org/pdf/2411.12537 | https://iclr.cc/media/iclr-2025/Slides/31836.pdf |
miniCTX - NEURAL THEOREM PROVING WITH (LONG -) CONTEXTS | 2,025 | ICLR | Oral | 5 | null | 5 | Yes | null | 49,565 | 26 | Neural Theorem Proving;Contextual Reasoning;Interactive Theorem Provers;Fine-Tuning Language Models | https://arxiv.org/pdf/2408.03350 | https://iclr.cc/media/iclr-2025/Slides/31870.pdf |
Can MLLMs Reason in Multimodality | 2,025 | ICML | Oral | 8 | null | 3 | Yes | null | 45,918 | 45 | Multimodal Reasoning;Benchmark Development;Cross-Modal Reasoning Tasks;Large Language Models Evaluation | https://arxiv.org/pdf/2501.05444 | https://icml.cc/media/icml-2025/Slides/47178_idf0Qr8.pdf |
Foundation Model Insights and a Multi-Model Approach for | 2,025 | ICML | Oral | 4 | null | 3 | Yes | null | 50,190 | 36 | One-Shot Subset Selection;Foundation Models;Fine-Grained Image Datasets;Data Efficiency in Deep Learning | https://arxiv.org/pdf/2506.14473 | https://icml.cc/media/icml-2025/Slides/47211.pdf |
Learning with Expected Signatures - Theory and Applications | 2,025 | ICML | Oral | 3 | null | 3 | Yes | null | 46,366 | 13 | Expected Signatures;Time Series Analysis;Martingale Processes;Probabilistic Machine Learning | https://arxiv.org/pdf/2505.20465 | https://icml.cc/media/icml-2025/Slides/43548.pdf |
LoRA-One - One-Step Full Gradient Could Suffice for Fine-Tuning Large | 2,025 | ICML | Oral | 5 | null | 5 | Yes | null | 45,295 | 24 | Low-Rank Adaptation;Gradient Descent Optimization;Fine-Tuning Large Language Models;Theory-Driven Algorithm Design | https://arxiv.org/pdf/2502.01235 | https://icml.cc/media/icml-2025/Slides/47237.pdf |
Model_Immunization_from_a_Condition_Number_Perspective | 2,025 | ICML | Oral | 3 | null | 3 | Yes | null | 42,978 | 20 | Model Immunization;Condition Number Analysis;Hessian Matrix Regularization;Non-Harmful Task Retention | https://arxiv.org/pdf/2505.23760 | https://icml.cc/media/icml-2025/Slides/47180.pdf |
Position - Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance | 2,025 | ICML | Oral | 9 | null | 4 | Yes | null | 44,267 | 9 | Model Licensing Practices;Legal Compliance in AI;Standardization of ML Licenses;Risks of License Noncompliance | https://raw.githubusercontent.com/mlresearch/v267/main/assets/duan25d/duan25d.pdf | https://icml.cc/media/icml-2025/Slides/40180_4s09QZJ.pdf |
Sundial - A Family of Highly Capable Time Series Foundation Models | 2,025 | ICML | Oral | 10 | null | 7 | Yes | null | 57,288 | 27 | Time Series Forecasting;Transformers in Time Series;Generative Forecasting Models;Probabilistic Prediction Techniques | https://arxiv.org/pdf/2502.00816 | https://icml.cc/media/icml-2025/Slides/45591_NN9Y4k8.pdf |
VideoRoPE - What Makes for Good Video Rotary Position Embedding | 2,025 | ICML | Oral | 7 | null | 5 | Yes | null | 48,167 | 17 | Rotary Position Embedding;Spatio-Temporal Analysis;Video Retrieval;Video Understanding | https://arxiv.org/pdf/2502.05173 | https://icml.cc/media/icml-2025/Slides/47183.pdf |
Adaptive Surrogate Gradients for Sequential | 2,025 | NeurIPS | Oral | 5 | null | 7 | No | null | 50,102 | 60 | Spiking Neural Networks;Surrogate Gradient Optimization;Reinforcement Learning;Neuromorphic Computing | https://arxiv.org/pdf/2510.24461 | https://nips.cc/media/neurips-2025/Slides/116055.pdf |
Dynam3D - Dynamic Layered 3D Tokens Empower VLM for Vision-and-Language Navigation | 2,025 | NeurIPS | Oral | 4 | null | 6 | Yes | null | 49,369 | 13 | Vision-and-Language Navigation;3D Object Recognition;Dynamic 3D Representation;Long-Term Environmental Memory | https://arxiv.org/pdf/2505.11383 | https://nips.cc/media/neurips-2025/Slides/115716.pdf |
GNNXEMPLAR - Exemplars to Explanations - Natural | 2,025 | NeurIPS | Oral | 11 | null | 3 | Yes | null | 70,794 | 41 | Graph Neural Networks;Global Explainability;Natural Language Processing;Exemplar Selection | https://arxiv.org/pdf/2509.18376 | https://neurips.cc/media/neurips-2025/Slides/116902.pdf |
OpenHOI - Open-World Hand-Object Interaction | 2,025 | NeurIPS | Oral | 3 | null | 5 | Yes | null | 44,988 | 20 | Open-World Hand-Object Interaction;Multimodal Large Language Models;Affordance-driven Diffusion Models;Complex Language Instruction Decomposition | https://arxiv.org/pdf/2505.18947 | https://neurips.cc/media/neurips-2025/Slides/120304.pdf |
ArcBench: ML Conference Oral Paper-Presentation Benchmark
This benchmark is from the paper Narrative-Driven Paper-to-Slide Generation via ArcDeck.
A curated benchmark dataset of 100 oral presentation paper-slide deck link pairs from top-tier machine learning conferences (CVPR, ICCV, ICLR, ICML, NeurIPS), spanning 2022–2025. Each entry provides rich metadata together with links to the original paper PDF and presentation slides, plus a script that downloads them all in one step.
Dataset Summary
This benchmark is designed to support research on multimodal document understanding, slide generation, paper-to-slide alignment, and LLM evaluation tasks. Papers were selected from oral presentations only — the highest-quality subset of each conference — and filtered to ensure rich content (≥3 figures, ≥3 tables) and availability of both the original paper PDF and presentation slides.
Dataset Structure
Files
benchmark.csv # Metadata + source links for all 100 papers
download_pdfs.py # Downloads every paper/slide PDF from its source link
download_openreview.py # Companion downloader for the OpenReview-hosted papers
papers/ # Created by the download script: 100 paper PDFs
└── paper{i}_{Title}_{Conference}_{Year}.pdf
slides/ # Created by the download script: 100 slide PDFs
└── slide{i}_{Title}_{Conference}_{Year}.pdf
The papers/ and slides/ folders are populated by running the download script
(see Downloading the PDFs).
Metadata Fields (benchmark.csv)
| Column | Type | Description |
|---|---|---|
Paper Title |
string | Full paper title |
Year |
int | Publication year (2022–2025) |
Conference |
string | Conference name (CVPR, ICCV, ICLR, ICML, NeurIPS) |
Presentation Type |
string | Always Oral in this benchmark |
Number of Figures |
int | Number of figures in the paper |
Number of Equations |
int | Number of equations in the paper |
Number of Tables |
int | Number of tables in the paper |
Appendix |
string | Whether paper has an appendix (Yes/No) |
Slide Animations |
string | Notes on slide animations, if any |
Character_Count |
int | Total character count of the paper (extracted via PDF) |
Number_of_Slides |
int | Number of pages/slides in the slide PDF |
Topics |
string | Semicolon-separated LLM-extracted research topics |
Paper PDF URL |
string | Link to the original paper PDF (arXiv, proceedings, OpenReview, …) |
Slides URL |
string | Link to the original presentation slides PDF |
Naming Convention
Files are named as {type}{index}_{CleanTitle}_{Conference}_{Year}.pdf where:
indexis 0-based, consistent acrosspapers/andslides/for matched pairsCleanTitlehas special characters removed and spaces replaced by underscores (max 100 chars)
The download script reconstructs these exact filenames from benchmark.csv, so
file i always corresponds to row i of the metadata.
Downloading the PDFs
You can download the PDFs from their original sources with the included script.
# Download all 100 papers + 100 slides into ./papers and ./slides
python download_pdfs.py
Useful options:
python download_pdfs.py --type slides # slides only (or: papers, both)
python download_pdfs.py --indices 0,5,84 # just a few entries
python download_pdfs.py --limit 10 # first 10 entries
python download_pdfs.py --out /data/arcbench # choose the output directory
python download_pdfs.py --workers 8 # more parallelism
python download_pdfs.py --force # re-download existing files
The script verifies every download is a real PDF, writes atomically, and
skips files that are already present, so it is safe to re-run to resume an
interrupted download. Any files it could not fetch are listed in
download_failures.csv.
A note on versions
Links point to the canonical, live source for each work. For papers hosted on arXiv this is the latest revision, which may differ slightly from the exact PDF originally archived for the benchmark (updated figures, camera-ready edits, etc.). The content is the same paper. Slide decks served by conference media servers are typically byte-for-byte identical.
Dataset Statistics
Distribution by Conference
| Conference | Papers |
|---|---|
| ICML | 51 |
| ICLR | 31 |
| NeurIPS | 12 |
| ICCV | 4 |
| CVPR | 2 |
Distribution by Year
| Year | Papers |
|---|---|
| 2022 | 15 |
| 2023 | 15 |
| 2024 | 26 |
| 2025 | 44 |
Content Statistics
| Metric | Mean | Min | Max |
|---|---|---|---|
| Figures per paper | 6.0 | 3 | 18 |
| Tables per paper | 5.3 | 3 | — |
| Slides per paper | 27.5 | 8 | 85 |
| Characters per paper | 50,411 | — | — |
- 92% of papers include an appendix
- 100% are oral presentations
Top Research Topics
Extracted via GPT-4o-mini from paper abstracts:
Contrastive Learning · Graph Neural Networks · Causal Inference · Multimodal Large Language Models · Federated Learning · Sampling Efficiency · Reinforcement Learning · Diffusion Models · Self-Supervised Learning · Vision-Language Models
Selection Criteria
Papers were selected using the following filters applied to a broader 994-paper dataset:
- Presentation type: Oral only
- Minimum figures: ≥ 3
- Minimum tables: ≥ 3
- Original paper available: Must have the full (non-anonymized) version
- Balanced sampling: Proportional stratified sampling across year × conference to reach exactly 100 papers
Intended Uses
This dataset is suited for:
- Slide generation / paper-to-slide summarization: Given
papers/, generate slides comparable toslides/ - Slide-grounded QA: Answer questions about a paper using its slides as context
- Cross-modal retrieval: Match papers to their corresponding slides
- LLM evaluation: Benchmark LLM understanding of dense scientific documents
- Multimodal document analysis: Study relationships between figures, tables, equations, and slide content
Source
Papers were collected from official proceedings of:
Citation
If you use this dataset in your research, please cite:
@article{ozden2026arcdeck,
title = {Narrative-Driven Paper-to-Slide Generation via ArcDeck},
author = {Ozden, Tarik Can and VS, Sachidanand and Horoz, Furkan
and Kara, Ozgur and Kim, Junho and Rehg, James M.},
journal = {arXiv preprint arXiv:2604.11969},
year = {2026}
}
License
The benchmark metadata (benchmark.csv), the source links, and the
download scripts in this repository are released under the MIT license.
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