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Found 7 papers, 3 papers with code
Code Models are Zero-shot Precondition Reasoners
One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point.
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning
Recently, there has been an increasing interest in automated prompt optimization based on reinforcement learning (RL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Preserving Linear Separability in Continual Learning by Backward Feature Projection
To achieve a better stability-plasticity trade-off, we propose Backward Feature Projection (BFP), a method for continual learning that allows the new features to change up to a learnable linear transformation of the old features.
Towards More Objective Evaluation of Class Incremental Learning: Representation Learning Perspective
While the common method for evaluating CIL algorithms is based on average test accuracy for all learned classes, we argue that maximizing accuracy alone does not necessarily lead to effective CIL algorithms.
ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification
Contrastive learning has led to substantial improvements in the quality of learned embedding representations for tasks such as image classification.
EDDA: Explanation-driven Data Augmentation to Improve Explanation Faithfulness
Recent years have seen the introduction of a range of methods for post-hoc explainability of image classifier predictions.
Online Class-Incremental Continual Learning with Adversarial Shapley Value
As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption.
