Ying Wu

Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering

2018 · arXiv (Cornell University)

In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized by an And-Or graph (AOG). This interpretable AOG representation …

Latent Space Energy-Based Model of Symbol-Vector Coupling for Text Generation and Classification

2021 · arXiv (Cornell University)

We propose a latent space energy-based prior model for text generation and classification. The model stands on a generator network that generates the text sequence based on a continuous latent vector. The energy term of …

Iterative Teacher-Aware Learning

2021 · arXiv (Cornell University)

In human pedagogy, teachers and students can interact adaptively to maximize communication efficiency. The teacher adjusts her teaching method for different students, and the student, after getting familiar with the teacher's instruction mechanism, can infer …

Latent Diffusion Energy-Based Model for Interpretable Text Modeling

2022 · arXiv (Cornell University)

Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in generative modeling. Fueled by its flexibility in the formulation and strong modeling power of the latent space, recent works built …

Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning

2022 · arXiv (Cornell University)

Mathematical reasoning, a core ability of human intelligence, presents unique challenges for machines in abstract thinking and logical reasoning. Recent large pre-trained language models such as GPT-3 have achieved remarkable progress on mathematical reasoning tasks …

LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning

2024 · arXiv (Cornell University)

Conventional Task and Motion Planning (TAMP) approaches rely on manually crafted interfaces connecting symbolic task planning with continuous motion generation. These domain-specific and labor-intensive modules are limited in addressing emerging tasks in real-world settings. Here, …

INTERPRET: Interactive Predicate Learning from Language Feedback for Generalizable Task Planning

2024

Learning abstract state representations and knowledge is crucial for long-horizon robot planning.We present Inter-PreT, an Large Language Model (LLM)-powered framework for robots to learn symbolic predicates from language feedback of human non-experts during embodied interaction.The …

SSResNeXt: A Novel Deep Learning Architecture for Multi-class Breast Cancer Pathological Image Classification

2024 · Journal of Computing and Information Technology

Multi-class classification of breast cancer pathological images remains challenging due to complex image features and limited datasets. This study proposes SSResNeXt, a novel deep learning architecture incorporating a new Small-SE-ResNeXt Block with asymmetric convolutions and …

Beyond Pass@1: Self-Play with Variational Problem Synthesis Sustains RLVR

2025 · arXiv (Cornell University)

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a key paradigm for post-training Large Language Models (LLMs), particularly for complex reasoning tasks. However, vanilla RLVR training has been shown to improve Pass@1 performance …