2 citations · 5 across the 13 of their papers we have counts for
13 papers
Hyperbolic Neural Population Geometry Benefits Computation
Dennis Wu, Yi-Chun Hung, Braden Yuille +2
Neural population geometry shapes downstream computation. Recent empirical findings in neurobiology suggest that a hyperbolic structure underlies population activity in the hippoca…
AdvEvo-MARL: Shaping Internalized Safety through Adversarial Co-Evolution in Multi-Agent Reinforcement Learning
Zhenyu Pan, Yiting Zhang, Zhuo Liu +13
LLM-based multi-agent systems excel at planning, tool use, and role coordination, but their openness and interaction complexity also expose them to jailbreak, prompt-injection, and…
StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars
Weijian Li, Hong-Yu Chen, Nabeel Rehemtulla +6
Current time series foundation model (TSFM) training corpora largely omit data with certain complexities like irregular temporal sampling. Astronomical time series of stellar fluxe…
Evo-MARL: Co-Evolutionary Multi-Agent Reinforcement Learning for Internalized Safety
Zhenyu Pan, Yiting Zhang, Yutong Zhang +8
Multi-agent systems (MAS) built on multimodal large language models exhibit strong collaboration and performance. However, their growing openness and interaction complexity pose se…
Transformers and Their Roles as Time Series Foundation Models
Dennis Wu, Yihan He, Yuan Cao +2
We give a comprehensive analysis of transformers as time series foundation models, focusing on their approximation and generalization capabilities. First, we demonstrate that there…
Learning Spectral Methods by Transformers
Yihan He, Yuan Cao, Hong-Yu Chen +3
Transformers demonstrate significant advantages as the building block of modern LLMs. In this work, we study the capacities of Transformers in performing unsupervised learning. We…
Transformers Simulate MLE for Sequence Generation in Bayesian Networks
Yuan Cao, Yihan He, Dennis Wu +3
Transformers have achieved significant success in various fields, notably excelling in tasks involving sequential data like natural language processing. Despite these achievements,…
Provably Optimal Memory Capacity for Modern Hopfield Models: Transformer-Compatible Dense Associative Memories as Spherical Codes
Jerry Yao-Chieh Hu, Dennis Wu, Han Liu
We study the optimal memorization capacity of modern Hopfield models and Kernelized Hopfield Models (KHMs), a transformer-compatible class of Dense Associative Memories. We present…
Nonparametric Modern Hopfield Models
Jerry Yao-Chieh Hu, Bo-Yu Chen, Dennis Wu +2
We present a nonparametric interpretation for deep learning compatible modern Hopfield models and utilize this new perspective to debut efficient variants. Our key contribution ste…
Uniform Memory Retrieval with Larger Capacity for Modern Hopfield Models
Dennis Wu, Jerry Yao-Chieh Hu, Teng-Yun Hsiao +1
We propose a two-stage memory retrieval dynamics for modern Hopfield models, termed , with enhanced memory capacity. Our key contribution is a learnable feat…
STanHop: Sparse Tandem Hopfield Model for Memory-Enhanced Time Series Prediction
Dennis Wu, Jerry Yao-Chieh Hu, Weijian Li +2
We present STanHop-Net (Sparse Tandem Hopfield Network) for multivariate time series prediction with memory-enhanced capabilities. At the heart of our approach is STanHop, a novel…
On Sparse Modern Hopfield Model
Jerry Yao-Chieh Hu, Donglin Yang, Dennis Wu +3
We introduce the sparse modern Hopfield model as a sparse extension of the modern Hopfield model. Like its dense counterpart, the sparse modern Hopfield model equips a memory-retri…
HonestBait: Forward References for Attractive but Faithful Headline Generation
Chih-Yao Chen, Dennis Wu, Lun-Wei Ku
Current methods for generating attractive headlines often learn directly from data, which bases attractiveness on the number of user clicks and views. Although clicks or views do r…