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Found 14 papers, 5 papers with code
Understanding R1-Zero-Like Training: A Critical Perspective
DeepSeek-R1-Zero has shown that reinforcement learning (RL) at scale can directly enhance the reasoning capabilities of LLMs without supervised fine-tuning.
Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k
With this model, we demonstrate that the cost of training a top-performing video generation model is highly controllable.
Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger
Large language models (LLMs) have shown remarkable emergent capabilities, transforming the execution of functional tasks by leveraging external tools for complex problems that require specialized processing or real-time data.
Improving Environment Novelty Quantification for Effective Unsupervised Environment Design
Unsupervised Environment Design (UED) formalizes the problem of autocurricula through interactive training between a teacher agent and a student agent.
A Survey of Foundation Models for Music Understanding
Music is essential in daily life, fulfilling emotional and entertainment needs, and connecting us personally, socially, and culturally.
Multi-Granularity and Multi-modal Feature Interaction Approach for Text Video Retrieval
To address this, we propose a novel multi-granularity feature interaction module called MGFI, consisting of text-frame and word-frame, for video-text representations alignment.
Unlocking Large Language Model's Planning Capabilities with Maximum Diversity Fine-tuning
To address this challenge, we propose the Maximum Diversity Fine-Tuning (MDFT) strategy to improve the sample efficiency of fine-tuning in the planning domain.
Controllable Talking Face Generation by Implicit Facial Keypoints Editing
Audio-driven talking face generation has garnered significant interest within the domain of digital human research.
ODD: A Benchmark Dataset for the Natural Language Processing based Opioid Related Aberrant Behavior Detection
Experimental results show that the prompt-tuning models outperformed the fine-tuning models in most categories and the gains were especially higher among uncommon categories (Suggested Aberrant Behavior, Confirmed Aberrant Behaviors, Diagnosed Opioid Dependence, and Medication Change).
ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT
The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format.
Diversity Induced Environment Design via Self-Play
In this paper, we aim to introduce diversity in the Unsupervised Environment Design (UED) framework.
Generalization through Diversity: Improving Unsupervised Environment Design
Agent decision making using Reinforcement Learning (RL) heavily relies on either a model or simulator of the environment (e. g., moving in an 8x8 maze with three rooms, playing Chess on an 8x8 board).
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
Facilitating human-wildlife cohabitation through conflict prediction
With increasing world population and expanded use of forests as cohabited regions, interactions and conflicts with wildlife are increasing, leading to large-scale loss of lives (animal and human) and livelihoods (economic).
