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
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
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query ShiftCode1
DER: Dynamically Expandable Representation for Class Incremental LearningCode1
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
Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and ReasoningCode1
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
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot LearningCode1
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
Dynamic Few-Shot Visual Learning without ForgettingCode1
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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