SOTAVerified

Novel Concepts

Measures the ability of models to uncover an underlying concept that unites several ostensibly disparate entities, which hopefully would not co-occur frequently. This provides a limited test of a model's ability to creatively construct the necessary abstraction to make sense of a situation that it cannot have memorized in training.

Source: BIG-bench

Papers

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Showing 101–150 of 158 papers

TitleStatusHype
A Closer Look at Rehearsal-Free Continual Learning—0
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations—0
Emergence of hierarchical reference systems in multi-agent communicationCode0
Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications—0
Generative Pre-Trained Transformer for Design Concept Generation: An Exploration—0
Few-Shot Novel Concept Learning for Semantic Parsing—0
CoSe-Co: Sentence Conditioned Generative CommonSense Contextualizer for Language Models—0
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods—0
Decision Making Using Rough Set based Spanning Sets for a Decision System—0
Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting—0
Open-Set Representation Learning through Combinatorial Embedding—0
Neural Concept Formation in Knowledge GraphsCode0
A Clustering-based Framework for Classifying Data StreamsCode0
How Should Agents Ask Questions For Situated Learning? An Annotated Dialogue CorpusCode0
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classificationCode0
Linguistically Routing Capsule Network for Out-of-Distribution Visual Question Answering—0
Enhancing Balanced Graph Edge Partition with Effective Local Search—0
SketchEmbedNet: Learning Novel Concepts by Imitating DrawingsCode0
Dialog Policy Learning for Joint Clarification and Active Learning Queries—0
Characterizing an Analogical Concept Memory for Architectures Implementing the Common Model of Cognition—0
Revisit Systematic Generalization via Meaningful LearningCode0
Contextual Blocking Bandits—0
Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging—0
FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers—0
Direct and indirect transactions and requirements—0
Simple and Lightweight Human Pose EstimationCode0
Knowledge Graph Transfer Network for Few-Shot RecognitionCode0
Structure Matters: Towards Generating Transferable Adversarial Images—0
Meta-Learning to Detect Rare Objects—0
Prototype Recalls for Continual Learning—0
Meta-Learning by Hallucinating Useful Examples—0
Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks—0
Resonant Machine Learning Based on Complex Growth Transform Dynamical Systems—0
Task-Aware Feature Generation for Zero-Shot Compositional LearningCode0
Variational Prototype Replays for Continual LearningCode0
Task-Driven Modular Networks for Zero-Shot Compositional LearningCode0
A Provable Defense for Deep Residual NetworksCode0
25 years of criticality in neuroscience -- established results, open controversies, novel concepts—0
Situational Grounding within Multimodal Simulations—0
Understanding MCMC Dynamics as Flows on the Wasserstein SpaceCode0
Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs—0
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts—0
Characterizing the Influence of Features on Reading Difficulty Estimation for Non-native Readers—0
Can Machines Design? An Artificial General Intelligence Approach—0
Multi-level Semantic Feature Augmentation for One-shot LearningCode0
Decoupled Novel Object CaptionerCode0
Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel ConceptsCode0
Privacy-Enabled Biometric Search—0
Zero-Shot Learning by Generating Pseudo Feature Representations—0
Sequential Local Learning for Latent Graphical Models—0
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