Search Results for author:
Found 11 papers, 5 papers with code
Clear Preferences Leave Traces: Reference Model-Guided Sampling for Preference Learning
Direct Preference Optimization (DPO) has emerged as a de-facto approach for aligning language models with human preferences.
Map2Text: New Content Generation from Low-Dimensional Visualizations
Low-dimensional visualizations, or "projection maps" of datasets, are widely used across scientific research and creative industries as effective tools for interpreting large-scale and complex information.
MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows
Scientific innovation relies on detailed workflows, which include critical steps such as analyzing literature, generating ideas, validating these ideas, interpreting results, and inspiring follow-up research.
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues
Our TOD-Flow graph learns what a model can, should, and should not predict, effectively reducing the search space and providing a rationale for the model's prediction.
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
+2
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 Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective
Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data while not forgetting past learned classes.
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.
