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Core AI

68,645 papers (13 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Multimodal Learning (14,926) Large Language Models (9,515) Interpretability (8,292) Foundation Models (5,277) Agent Systems (4,710) Causal Inference (3,958) Multi-Agent Systems (3,842) AI Safety (3,602) Planning (3,482) Reasoning (3,360) Human-AI Interaction (2,465) Model Compression (2,258) Natural Language Processing (2,186) Responsible AI (2,130) Computer Vision (1,901) Game AI (1,846) Reinforcement Learning (1,698) Adversarial Learning (1,500) Autonomous Vehicles (1,488) Language (1,345) Fairness (1,279) Game Theory (1,129) Robotics (1,087) Dialogue Systems (1,066) Efficient Computing (956) Vision-Language Models (928) Knowledge Graph (735) Procedural Generation (709) Trajectory Prediction (676) Evaluation (655) Privacy (617) Natural Language Generation (573) Information Retrieval (541) Memory (526) Decision Making (480) Safety (474) Knowledge Representation (409) Knowledge (386) Information Extraction (378) Speech Processing (310) Knowledge Graphs (282) Security (256) Uncertainty Quantification (246) Representation Learning (239) Retrieval-Augmented Generation (225) Knowledge Editing (200) Question Answering (199) Attention (198) Robustness (186) Knowledge Distillation (185) Code Generation (168) Explainability (142) Brain-Computer Interface (141) Neural Networks (136) Anomaly Detection (132) Automated Reasoning (84) Natural Language Understanding (81) Multi-Lingual Learning (79) Education (64) Machine Translation (57) Learning Paradigms (57) Speech Recognition (55) Risk Management (51) Visual Question Answering (49) Communication (35) Motion Analysis (32) Natural Language Inference (29) Sentiment Analysis (19) Representation (17)

Papers per year

1
11
99
273
4,063
17,181
2000
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Papers (including subtopics)

Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach AAAI 2021 Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning ICLR 2021 Explaining by Removing: A Unified Framework for Model Explanation JMLR 2021 Explaining Classifiers Using Adversarial Perturbations on the Perceptual Ball CVPR 2021 Explaining Contextualization in Language Models using Visual Analytics ACL 2021 Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation AAAI 2021 Explaining Deep Learning Models for Speech Enhancement Interspeech 2021 Explaining Errors in Machine Translation with Absolute Gradient Ensembles EMNLP 2021 Explaining Explanations: Axiomatic Feature Interactions for Deep Networks JMLR 2021 Explaining Hyperparameter Optimization via Partial Dependence Plots NeurIPS 2021 Explaining Local, Global, and Higher-Order Interactions in Deep Learning ICCV 2021 Explaining Neural Matrix Factorization with Gradient Rollback AAAI 2021 Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks NAACL 2021 Explaining Relationships Between Scientific Documents ACL 2021 Explaining the Efficacy of Counterfactually Augmented Data ICLR 2021 Explaining Time Series Predictions with Dynamic Masks ICML 2021 Explain Me the Painting: Multi-Topic Knowledgeable Art Description Generation ICCV 2021 Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability AAAI 2021 Explanation in Constraint Satisfaction: A Survey IJCAI 2021 Explanations for Monotonic Classifiers. ICML 2021 Explanations for Occluded Images ICCV 2021 Explicable Reward Design for Reinforcement Learning Agents NeurIPS 2021 Explicit Knowledge Incorporation for Visual Reasoning CVPR 2021 Exploiting Definitions for Frame Identification EACL 2021 Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution ACL 2021