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diff=lfs merge=lfs -text +slides/slide8_Rethinking_Image-Scaling_Attacks_ICML_2022.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide90_Learning_with_Expected_Signatures_-_Theory_and_Applications_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide91_LoRA-One_-_One-Step_Full_Gradient_Could_Suffice_for_Fine-Tuning_Large_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide92_Model_Immunization_from_a_Condition_Number_Perspective_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide93_Position_-_Current_Model_Licensing_Practices_are_Dragging_Us_into_a_Quagmire_of_Legal_Noncompliance_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide94_Sundial_-_A_Family_of_Highly_Capable_Time_Series_Foundation_Models_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide95_VideoRoPE_-_What_Makes_for_Good_Video_Rotary_Position_Embedding_ICML_2025.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide96_Adaptive_Surrogate_Gradients_for_Sequential_NeurIPS_2025.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide97_Dynam3D_-_Dynamic_Layered_3D_Tokens_Empower_VLM_for_Vision-and-Language_Navigation_NeurIPS_2025.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide98_GNNXEMPLAR_-_Exemplars_to_Explanations_-_Natural_NeurIPS_2025.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide99_OpenHOI_-_Open-World_Hand-Object_Interaction_NeurIPS_2025.pdf filter=lfs diff=lfs merge=lfs -text +slides/slide9_RieszNet_and_ForestRiesz_-_Automatic_Debiased_ICML_2022.pdf filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0ae8c7880cc91b5b6be8a7376f42bcbf7feb56b5 --- /dev/null +++ b/README.md @@ -0,0 +1,197 @@ +--- +license: mit +language: + - en +tags: + - machine-learning + - academic-papers + - slides + - multimodal + - benchmark + - nlp + - computer-vision + - oral-presentations +pretty_name: ArcBench ML Conference Oral Paper-Presentation Benchmark +size_categories: + - n<1K +dataset_info: + features: + - name: Paper Title + dtype: string + - name: Year + dtype: int32 + - name: Conference + dtype: string + - name: Presentation Type + dtype: string + - name: Number of Figures + dtype: int32 + - name: Number of Equations + dtype: int32 + - name: Number of Tables + dtype: int32 + - name: Appendix + dtype: string + - name: Slide Animations + dtype: string + - name: Character_Count + dtype: int32 + - name: Number_of_Slides + dtype: int32 + - name: Topics + dtype: string + splits: + - name: train + num_examples: 100 +--- + +# ArcBench: ML Conference Oral Paper-Presentation Benchmark + +A curated benchmark dataset of **100 oral presentation papers** from top-tier machine learning conferences (CVPR, ICCV, ICLR, ICML, NeurIPS), spanning 2022–2025. Each entry includes the full paper PDF, presentation slides PDF, and rich metadata. + +[![arXiv](https://img.shields.io/badge/arXiv-2411.17957-b31b1b.svg)](https://arxiv.org/abs/2411.17957) +[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://arcdeck.org/) +[![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Dataset-yellow)](https://huggingface.co/datasets/ArcDeck/ArcBench) + +--- + +## 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 + +``` +EvaluationBenchmark/ +├── benchmark.csv # Metadata for all 100 papers +├── papers/ # 100 original full-length paper PDFs +│ └── paper{i}_{Title}_{Conference}_{Year}.pdf +└── slides/ # 100 presentation slide PDFs + └── slide{i}_{Title}_{Conference}_{Year}.pdf +``` + +### 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 | + +### Naming Convention + +Files are named as `{type}{index}_{CleanTitle}_{Conference}_{Year}.pdf` where: +- `index` is 0-based, consistent across `papers/` and `slides/` for matched pairs +- `CleanTitle` has special characters removed and spaces replaced by underscores (max 100 chars) + +--- + +## 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 to `slides/` +- **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: +- [ICML](https://icml.cc) (2022–2025) +- [ICLR](https://iclr.cc) (2024–2025) +- [NeurIPS](https://neurips.cc) (2022–2025) +- [CVPR](https://cvpr.thecvf.com) (2024–2025) +- [ICCV](https://iccv2023.thecvf.com) (2025) + +--- + +## Citation + +If you use this dataset in your research, please cite: + +```bibtex +@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 + +MIT. +Individual paper and slide PDFs remain under their original authors' copyright. Please consult each paper's license before redistribution. diff --git a/benchmark.csv b/benchmark.csv new file mode 100644 index 0000000000000000000000000000000000000000..64ac55078b2dfa1c959cd828ba24afb79dac373d --- /dev/null +++ b/benchmark.csv @@ -0,0 +1,101 @@ +Paper Title,Year,Conference,Presentation Type,Number of Figures,Number of Equations,Number of Tables,Appendix,Slide Animations,Character_Count,Number_of_Slides,Topics +3DLinker - An E(3) Equivariant Variational Autoencoder,2022,ICML,Oral,4,,3,Yes,,45879,19,Molecular Linker Design;E(3) Equivariant Graph Variational Autoencoder;Conditional Generative Models;3D Molecular Structure Prediction +"Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness",2022,ICML,Oral,4,,3,Yes,,44347,47,Counterfactual Inference;Data Integration;Fair Representation Learning;Batch Effect Correction +Correct-N-Contrast - A Contrastive Approach for Improving Robustness to Spurious Correlations,2022,ICML,Oral,7,,4,Yes,,44964,78,Robust Machine Learning;Spurious Correlations;Contrastive Learning;Representation Alignment +Learning inverse folding from millions of predicted structures,2022,ICML,Oral,7,,3,Yes,,42125,18,Inverse Folding Prediction;Protein Structure Prediction;Generative Models for Protein Design;Sequence-to-Sequence Learning in Protein Engineering +Monarch - Expressive Structured Matrices for Efficient and Accurate Training,2022,ICML,Oral,5,,8,Yes,,43885,44,Structured Matrices;Sparse Training;Efficient Neural Network Training;Matrix Approximation Techniques +POEM - Out-of-Distribution Detection with Posterior Sampling,2022,ICML,Oral,3,,3,Yes,,54444,34,Out-of-Distribution Detection;Posterior Sampling;Outlier Detection;Neural Network Regularization +Path-Gradient Estimators for Continuous Normalizing Flows,2022,ICML,Oral,5,,3,Yes,,39565,29,Path-Gradient Estimators;Continuous Normalizing Flows;Variational Inference;High-Dimensional Systems +Privacy for Free - How does Dataset Condensation Help Privacy,2022,ICML,Oral,8,,3,Yes,,53800,24,Dataset Condensation;Differential Privacy;Membership Inference Attacks;Data Privacy in Machine Learning +Rethinking Image-Scaling Attacks,2022,ICML,Oral,9,,4,Yes,,47649,44,Image Scaling Algorithms;Adversarial Attacks;Machine Learning Vulnerabilities;Decision-Based Black-Box Attacks +RieszNet and ForestRiesz - Automatic Debiased,2022,ICML,Oral,3,,4,Yes,,46414,33,Automatic Debiasing;Riesz Representation;Neural Networks;Random Forests +To Smooth or Not When Label Smoothing Meets Noisy Labels,2022,ICML,Oral,5,,6,Yes,,46876,27,Label Smoothing;Noisy Label Learning;Regularization Techniques;Negative Label Smoothing +Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning,2022,ICML,Oral,8,,4,No,,51214,20,Topology-Aware Network Pruning;Graph Neural Networks;Reinforcement Learning;Model Compression +Understanding Dataset Difficulty with V-Usable Information,2022,ICML,Oral,9,,4,Yes,,58804,32,Dataset Difficulty Estimation;V-Usable Information;Pointwise V-Information;NLP Benchmark Analysis +Unified Scaling Laws for Routed Language Models,2022,ICML,Oral,13,,6,Yes,,45020,84,Scaling Laws in Language Models;Routing Networks;Neural Network Architecture Evaluation;Effective Parameter Count +"data2vec - A General Framework for Self-supervised Learning in Speech, Vision and Language",2022,ICML,Oral,3,,6,Yes,,47928,14,Self-supervised Learning;Multimodal Learning;Contextualized Representations;Speech Recognition +Adversarial Example Does Good - Preventing Painting Imitation from,2023,ICML,Oral,5,,4,Yes,,43771,12,Adversarial Training;Diffusion Models;Copyright Protection in AI Art;Image Synthesis +Audio Pre-Training with Acoustic Tokenizers,2023,ICML,Oral,4,,3,Yes,,49406,28,Self-Supervised Learning for Audio;Acoustic Tokenization;Audio Representation Learning;Audio Classification Benchmarking +Bidirectional Adaptation for Robust Semi-Supervised Learning,2023,ICML,Oral,3,,3,Yes,,52501,8,Robust Semi-Supervised Learning;Distribution Adaptation;Debiased Pseudo-Labeling;Theoretical Framework for SSL +Evaluating Self-Supervised Learning via Risk Decomposition,2023,ICML,Oral,13,,4,Yes,,58816,28,Self-Supervised Learning;Risk Decomposition;Error Analysis in Representation Learning;Evaluation Metrics for SSL Models +Fast Inference from Transformers via Speculative Decoding,2023,ICML,Oral,5,,3,Yes,,34452,14,Speculative Decoding;Parallel Inference;Autoregressive Models +Instant Soup - Cheap Pruning Ensembles in A Single Pass Can,2023,ICML,Oral,3,,7,Yes,,46357,8,Pruning Ensembles;Lottery Ticket Hypothesis;Model Fine-Tuning;Large Pre-trained Transformers +ODS - Test-Time Adaptation in the Presence of Open-World Data Shift,2023,ICML,Oral,7,,7,Yes,,43543,21,Test-Time Adaptation;Open-World Data Shift;Covariate and Label Distribution Shift +Pre-training for Speech Translation - CTC Meets Optimal Transport,2023,ICML,Oral,5,,10,Yes,,48994,21,Speech-to-Text Translation;Connectionist Temporal Classification;Optimal Transport;Siamese Neural Networks +Refining Generative Process with Discriminator Guidance,2023,ICML,Oral,15,,8,Yes,,45867,19,Score-based Diffusion Models;Discriminator-guided Learning;Generative Adversarial Networks (GANs) Alternative Approaches;Image Generation Techniques +Subequivariant Graph Reinforcement Learning in 3D Environments,2023,ICML,Oral,8,,5,Yes,,47869,21,Morphology-Agnostic Reinforcement Learning;Subequivariant Graph Neural Networks;3D Environment Exploration;Policy Optimization via Geometric Symmetry +Which Features are Learnt by Contrastive Learning,2023,ICML,Oral,6,,5,Yes,,54697,62,Contrastive Learning;Representation Learning;Feature Suppression;Class Collapse +Causal normalizing flows - from theory to practice,2023,NeurIPS,Oral,5,,3,Yes,,52327,67,Causal Inference;Normalizing Flows;Autoregressive Models;Counterfactual Reasoning +Conformal Meta-learners for Predictive Inference of,2023,NeurIPS,Oral,5,,3,Yes,,53153,25,Individual Treatment Effects;Conformal Prediction;Meta-Learning;Causal Inference +Learning Linear Causal Representations from Interventions,2023,NeurIPS,Oral,3,,3,Yes,,62186,31,Causal Inference;Identification of Latent Variables;Contrastive Learning;High-Dimensional Geometry in Causal Learning +QL ORA - Efficient Finetuning of Quantized LLMs,2023,NeurIPS,Oral,3,,8,Yes,,75353,20,Efficient Finetuning of Quantized Language Models;Low Rank Adaptation (LoRA);Memory Optimization Techniques;Chatbot Performance Evaluation +Learning to Segment Referred Objects from Narrated Egocentric Videos,2024,CVPR,Oral,4,,3,No,,55536,17,Weakly-Supervised Video Object Segmentation;Vision-Language Models;Contrastive Learning;Egocentric Video Analysis +CAMERAS AS RAYS - POSE ESTIMATION VIA RAY DIFFUSION,2024,ICLR,Oral,7,,3,Yes,,40716,21,Pose Estimation;Ray Diffusion;3D Reconstruction;Denoising Diffusion Models +CLIM ODE - C LIMATE AND WEATHER FORECASTING,2024,ICLR,Oral,8,,3,Yes,,35588,35,Physics-Informed Neural Networks;Weather Forecasting;Uncertainty Quantification;Spatiotemporal Modeling +LESS IS MORE - F EWER INTERPRETABLE REGION VIA,2024,ICLR,Oral,3,,4,Yes,,47332,18,Image Attribution;Submodular Optimization;Model Interpretability +METAGPT - M ETA PROGRAMMING FOR A MULTI-AGENT COLLABORATIVE FRAMEWORK,2024,ICLR,Oral,5,,6,Yes,,46728,17,Meta-Programming;Multi-Agent Systems;Large Language Models;Automated Problem Solving +"A Touch, Vision, and Language Dataset for Multimodal Alignment",2024,ICML,Oral,6,,4,Yes,,42012,48,Multimodal Representation Learning;Tactile Data Annotation;Vision-Touch Language Alignment;Generative Language Models +APT - Adaptive Pruning and Tuning Pretrained Language Models for,2024,ICML,Oral,3,,7,Yes,,48883,20,Adaptive Pruning;Efficient Fine-Tuning;Parameter-Efficient Models;Language Model Optimization +Arrows_of_Time_for_Large_Language_Models,2024,ICML,Oral,4,,5,Yes,,52767,85,Autoregressive Language Modeling;Time Asymmetry in Probabilistic Models;Information Theory in AI Systems;Sparse Representation in Neural Networks +Bottleneck-Minimal Indexing for Generative Document Retrieval,2024,ICML,Oral,6,,4,Yes,,45364,24,Generative Document Retrieval;Information-Theoretic Approaches;Rate-Distortion Theory;Neural Autoregressive Models +Candidate Pseudolabel Learning - Enhancing Vision-Language Models by,2024,ICML,Oral,7,,6,Yes,,48882,15,Candidate Pseudolabel Learning;Vision-Language Models;Unlabeled Data Fine-tuning;Prompt Tuning +Challenges in Training PINNs - A Loss Landscape Perspective,2024,ICML,Oral,8,,3,Yes,,53420,30,Physics-Informed Neural Networks;Loss Landscape Optimization;Gradient-Based Optimization Methods;Partial Differential Equations +Data-free Neural Representation Compression,2024,ICML,Oral,3,,5,Yes,,47302,18,Riemannian Geometry in Neural Networks;Data-free Neural Network Compression;Dynamic Systems and Neural Representation;Neuronal Interaction Modeling +ExCP - Extreme LLM Checkpoint Compression via Weight-Momentum Joint Shrinking,2024,ICML,Oral,5,,7,Yes,,42889,14,Checkpoint Compression;Weight-Momentum Optimization;Non-Uniform Quantization;Sparse Information Extraction +Expressivity and Generalization - Fragment-Biases for Molecular GNNs,2024,ICML,Oral,6,,7,Yes,,50175,23,Fragment-Biased Graph Neural Networks;Theoretic Expressivity Analysis;Molecular Property Prediction;Generalization in Machine Learning +Listenable Maps for Zero-Shot Audio Classifiers,2024,ICML,Oral,5,,3,Yes,,45423,42,Explainable AI;Zero-Shot Learning;Audio Classification;Post-Hoc Explanation Methods +MLLM-as-a-Judge - Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark,2024,ICML,Oral,9,,8,Yes,,61182,33,Multimodal Large Language Models;Benchmark Development;Judgment Biases in AI;Evaluation Metrics for AI Systems +MorphGrower - A Synchronized Layer-by-layer Growing Approach for,2024,ICML,Oral,5,,3,Yes,,49482,19,Neuronal Morphology Generation;Synchronized Layer-by-layer Growth;Topological Validity in Morphologies;Electrophysiological Response Simulation +Position - Rethinking Post-Hoc Search-Based Neural Approaches for Solving,2024,ICML,Oral,4,,3,Yes,,45092,25,Heatmap Generation for Optimization;Monte Carlo Tree Search;Traveling Salesman Problem;Combinatorial Optimization Methods +SparseTSF - Modeling Long-term Time Series Forecasting with 1k Parameters,2024,ICML,Oral,4,,7,Yes,,49044,18,Long-term Time Series Forecasting;Cross-Period Sparse Forecasting;Parameter Efficiency in Machine Learning Models;Generalization in Low-Resource Scenarios +Towards_Optimal_Adversarial_Robust_Q-learning_with_Bellman_Infinity-error,2024,ICML,Oral,5,,3,Yes,,54693,30,Adversarial Robustness in Reinforcement Learning;Optimal Robust Policy in Markov Decision Processes;Bellman Error Minimization Techniques;Deep Q-Networks Training Methods +Unified_Training_of_Universal_Time_Series_Forecasting_Transformers,2024,ICML,Oral,6,,7,Yes,,57564,39,Universal Time Series Forecasting;Transformer Architectures;Cross-Frequency Learning;Multivariate Time Series Analysis +Video-of-Thought - Step-by-Step Video Reasoning from Perception to Cognition,2024,ICML,Oral,8,,4,Yes,,59901,27,Video Understanding;Spatial-Temporal Reasoning;Multimodal Large Language Models;Cognitive Video Comprehension +AgentBoard - An Analytical Evaluation Board of,2024,NeurIPS,Oral,6,,7,Yes,,59530,62,Evaluation Frameworks for Large Language Models;Multi-Turn Interaction in AI Agents;Benchmarking of Agent Performance;Interpretable AI +DapperFL - Domain Adaptive Federated Learning with,2024,NeurIPS,Oral,4,,3,Yes,,52291,29,Federated Learning;Domain Adaptation;Model Pruning;Edge Computing +"Decompose, Analyze and Rethink",2024,NeurIPS,Oral,5,,4,Yes,,53419,12,Natural Language Processing;Reasoning in AI;Tree-based Question Decomposition;Large Language Models +You Only Cache Once - Decoder-Decoder Architectures for Language Models,2024,NeurIPS,Oral,10,,5,Yes,,44856,12,Decoder-Decoder Architectures;Key-Value Caching Optimization;Transformer Model Efficiency;Global Attention Mechanisms +Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning,2025,CVPR,Oral,8,,7,No,,51814,10,Federated Learning;Data Heterogeneity;Geometric Distribution Alignment;Sample Generation Techniques +Deterministic Object Pose Confidence Region Estimation,2025,ICCV,Oral,6,,6,No,,44266,27,6D Pose Estimation;Uncertainty Quantification;Deterministic Estimation Methods;Conformal Prediction +Diffusion Image Prior,2025,ICCV,Oral,7,,6,No,,35462,22,Diffusion Models;Image Restoration;Blind Image Restoration;Artifact Removal +Diving into the Fusion of Monocular Priors for Generalized Stereo Matching,2025,ICCV,Oral,4,,7,No,,48476,35,Stereo Matching;Monocular Depth Estimation;Depth Map Fusion;Ill-posed Problem Handling +Learning Streaming Video Representation via Multitask Training,2025,ICCV,Oral,3,,6,No,,62845,19,Streaming Video Representation;Multitask Learning;Online Action Detection;Video Question Answering +ACCELERATED TRAINING THROUGH ITERATIVE,2025,ICLR,Oral,6,,4,Yes,,44150,14,Highway Backpropagation;Residual Networks;Gradient Propagation Algorithms;Deep Learning Optimization +BOOSTER - T ACKLING HARMFUL FINE -TUNING FOR,2025,ICLR,Oral,3,,9,Yes,,61851,16,Harmful Fine-Tuning Attacks;Large Language Model Alignment;Loss Regularization Techniques +CHART MOE - M IXTURE OF DIVERSELY ALIGNED EX,2025,ICLR,Oral,11,,8,Yes,,58200,13,Chart Understanding;Mixture of Experts (MoE);Multimodal Large Language Models (MLLMs);Dataset Creation for Chart Analysis +CYBER HOST - A O NE-STAGE DIFFUSION FRAMEWORK,2025,ICLR,Oral,6,,3,Yes,,49641,21,Audio-Driven Talking Body Generation;Diffusion-Based Video Generation;Human Animation Synthesis;Region Attention Module in Animation +DEPT - D ECOUPLED EMBEDDINGS FOR PRE,2025,ICLR,Oral,5,,13,Yes,,67319,23,Decoupled Embeddings;Federated Learning;Language Model Pre-training;Communication-Efficient Training +FLAT REWARD IN POLICY PARAMETER SPACE IMPLIES,2025,ICLR,Oral,7,,3,Yes,,48254,30,Flat Reward Landscapes;Robustness in Reinforcement Learning;Policy Parameter Space Analysis;Generalization in Deep Neural Networks +GESUBNET - G ENE INTERACTION INFERENCE FOR,2025,ICLR,Oral,6,,4,Yes,,54038,21,Gene Interaction Inference;Disease Subtype Network Generation;Graph Neural Networks;Representation Learning +GRID MIX - E XPLORING SPATIAL MODULATION FOR,2025,ICLR,Oral,4,,6,Yes,,50700,20,Spatial Modulation;Neural Fields;Partial Differential Equations (PDE) Modeling;Domain Augmentation +IMPROVING PROBABILISTIC DIFFUSION MODELS WITH,2025,ICLR,Oral,4,,8,Yes,,51204,43,Probabilistic Diffusion Models;Optimal Covariance Matching;Denoising Distribution;Sampling Efficiency +IMPROVING PROBABILISTIC DIFFUSION MODELS WITH (2),2025,ICLR,Oral,4,,8,Yes,,51204,17,Probabilistic Diffusion Models;Diagonal Covariance Matching;Optimal Covariance Matching;Sampling Efficiency +Inference_Scaling_for_Long-Context_Retrieval_Augmented_Generation,2025,ICLR,Oral,8,,4,Yes,,46464,38,Long-Context Large Language Models;Retrieval Augmented Generation;Inference Scaling Laws;Optimal Computation Allocation +KNOWING YOUR TARGET - TARGET -AWARE TRANSFORMER MAKES BETTER SPATIO-T EMPORAL,2025,ICLR,Oral,6,,10,Yes,,52853,17,Spatio-Temporal Video Grounding;Target-Aware Transformers;Multimodal Feature Interactions;Object Query Initialization +LEARNING TO DISCRETIZE,2025,ICLR,Oral,6,,8,Yes,,49108,29,Diffusion Probabilistic Models;Sampling Efficiency;Neural Function Evaluations;Generative Model Optimization +MEASURING AND ENHANCING TRUSTWORTHINESS OF,2025,ICLR,Oral,3,,10,Yes,,65340,39,Trustworthiness Evaluation in LLMs;Retrieval-Augmented Generation (RAG);Prompting Methods for LLMs;Model Alignment Techniques +NEURAL PLANE - S TRUCTURED 3D R ECONSTRUCTION,2025,ICLR,Oral,7,,3,Yes,,48329,16,3D Plane Reconstruction;Neural Fields;Self-Supervised Learning;Semantic Segmentation +Navigating the Digital World as Humans Do,2025,ICLR,Oral,5,,9,Yes,,61854,26,Visual Grounding;Multimodal Learning;Graphical User Interface (GUI) Agents;Synthetic Data Generation +OPEN -VOCABULARY OBJECT DETECTION VIA,2025,ICLR,Oral,7,,6,Yes,,48188,12,Open-Vocabulary Object Detection;Knowledge Distillation;Vision and Language Integration;Transfer Learning +Open-YOLO 3D - Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation,2025,ICLR,Oral,4,,5,Yes,,38184,17,Open-Vocabulary 3D Instance Segmentation;2D Object Detection;Multi-View Image Processing;Real-Time Inference Techniques +PROBABILISTIC LEARNING TO DEFER - H ANDLING,2025,ICLR,Oral,5,,3,Yes,,47151,47,Learning to Defer;Probabilistic Modelling;Workload Distribution in Human-AI Cooperation;Handling Missing Annotations +REPRESENTATION ALIGNMENT FOR GENERATION,2025,ICLR,Oral,8,,5,Yes,,61444,19,Representation Alignment;Denoising Diffusion Models;Visual Representation Learning;Training Efficiency in Generative Models +SD-L ORA - S CALABLE DECOUPLED LOW-RANK ADAP,2025,ICLR,Oral,5,,5,Yes,,46379,28,Class Incremental Learning;Low-Rank Adaptation;Scalable Machine Learning;Continual Learning +SPIDER 2.0 - E VALUATING LANGUAGE MODELS ON,2025,ICLR,Oral,18,,18,Yes,,58210,23,Text-to-SQL Transformation;SQL Query Generation;Model Evaluation Frameworks;Enterprise Database Systems +STANDARD GAUSSIAN PROCESS IS ALL YOU NEED FOR,2025,ICLR,Oral,5,,4,Yes,,44476,24,Bayesian Optimization;Gaussian Processes;Matérn Kernels;High-Dimensional Optimization +TIME MIXER ++ - A G ENERAL TIME SERIES PATTERN,2025,ICLR,Oral,5,,10,Yes,,59545,18,Time Series Analysis;Pattern Extraction;Multi-Resolution Time Imaging;Anomaly Detection +TOWARD GUIDANCE -F REE AR V ISUAL GENERATION,2025,ICLR,Oral,8,,3,Yes,,60033,26,Classifier-Free Guidance;Autoregressive Visual Generation;Contrastive Learning;Multi-Modal Alignment +UNLOCKING STATE-TRACKING IN LINEAR RNN S,2025,ICLR,Oral,9,,5,Yes,,56524,22,Linear Recurrent Neural Networks;State-Tracking in Neural Networks;Eigenvalue Analysis in Neural Networks;Language Modeling +miniCTX - NEURAL THEOREM PROVING WITH (LONG -) CONTEXTS,2025,ICLR,Oral,5,,5,Yes,,49565,26,Neural Theorem Proving;Contextual Reasoning;Interactive Theorem Provers;Fine-Tuning Language Models +Can MLLMs Reason in Multimodality,2025,ICML,Oral,8,,3,Yes,,45918,45,Multimodal Reasoning;Benchmark Development;Cross-Modal Reasoning Tasks;Large Language Models Evaluation +Foundation Model Insights and a Multi-Model Approach for,2025,ICML,Oral,4,,3,Yes,,50190,36,One-Shot Subset Selection;Foundation Models;Fine-Grained Image Datasets;Data Efficiency in Deep Learning +Learning with Expected Signatures - Theory and Applications,2025,ICML,Oral,3,,3,Yes,,46366,13,Expected Signatures;Time Series Analysis;Martingale Processes;Probabilistic Machine Learning +LoRA-One - One-Step Full Gradient Could Suffice for Fine-Tuning Large,2025,ICML,Oral,5,,5,Yes,,45295,24,Low-Rank Adaptation;Gradient Descent Optimization;Fine-Tuning Large Language Models;Theory-Driven Algorithm Design +Model_Immunization_from_a_Condition_Number_Perspective,2025,ICML,Oral,3,,3,Yes,,42978,20,Model Immunization;Condition Number Analysis;Hessian Matrix Regularization;Non-Harmful Task Retention +Position - Current Model Licensing Practices are Dragging Us into a Quagmire of Legal Noncompliance,2025,ICML,Oral,9,,4,Yes,,44267,9,Model Licensing Practices;Legal Compliance in AI;Standardization of ML Licenses;Risks of License Noncompliance +Sundial - A Family of Highly Capable Time Series Foundation Models,2025,ICML,Oral,10,,7,Yes,,57288,27,Time Series Forecasting;Transformers in Time Series;Generative Forecasting Models;Probabilistic Prediction Techniques +VideoRoPE - What Makes for Good Video Rotary Position Embedding,2025,ICML,Oral,7,,5,Yes,,48167,17,Rotary Position Embedding;Spatio-Temporal Analysis;Video Retrieval;Video Understanding +Adaptive Surrogate Gradients for Sequential,2025,NeurIPS,Oral,5,,7,No,,50102,60,Spiking Neural Networks;Surrogate Gradient Optimization;Reinforcement Learning;Neuromorphic Computing +Dynam3D - Dynamic Layered 3D Tokens Empower VLM for Vision-and-Language Navigation,2025,NeurIPS,Oral,4,,6,Yes,,49369,13,Vision-and-Language Navigation;3D Object Recognition;Dynamic 3D Representation;Long-Term Environmental Memory +GNNXEMPLAR - Exemplars to Explanations - Natural,2025,NeurIPS,Oral,11,,3,Yes,,70794,41,Graph Neural Networks;Global Explainability;Natural Language Processing;Exemplar Selection +OpenHOI - Open-World Hand-Object Interaction,2025,NeurIPS,Oral,3,,5,Yes,,44988,20,Open-World Hand-Object Interaction;Multimodal Large Language Models;Affordance-driven Diffusion Models;Complex Language Instruction Decomposition diff --git a/papers/paper0_3DLinker_-_An_E3_Equivariant_Variational_Autoencoder_ICML_2022.pdf b/papers/paper0_3DLinker_-_An_E3_Equivariant_Variational_Autoencoder_ICML_2022.pdf 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