14 citations · 17 across the 9 of their papers we have counts for
9 papers
FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
Yuchen Hou, Yongshan Chen, Zhuowen Zou +4
Federated reinforcement learning enables decentralized agents to collaboratively improve policies or value estimates without exchanging raw trajectories. However, FedAvg-style para…
ReSAE: Residualized Sparse Autoencoders for Multi-Layer Transformer Interventions
Prathyush Poduval, Calvin Yeung, Neel Desai +1
Sparse autoencoders are usually trained one layer at a time, even though transformer residual stream activations are strongly coupled across depth. This creates a practical problem…
Residualized Temporal Sparse Autoencoders for Interpreting Diffusion Models
Calvin Yeung, Prathyush Poduval, Ali Zakeri +2
Text-to-image diffusion models generate images by iterative denoising, so their internal layers produce trajectories of activations rather than single static representations. Spars…
Geometric Priors for Generalizable World Models via Vector Symbolic Architecture
William Youngwoo Chung, Calvin Yeung, Hansen Jin Lillemark +3
A key challenge in artificial intelligence and neuroscience is understanding how neural systems learn representations that capture the underlying dynamics of the world. Most world…
Coupled Inference in Diffusion Models for Semantic Decomposition
Calvin Yeung, Ali Zakeri, Zhuowen Zou +1
Many visual scenes can be described as compositions of latent factors. Effective recognition, reasoning, and editing often require not only forming such compositional representatio…
Hyperdimensional Quantum Factorization
Prathyush Poduval, Zhuowen Zou, Alvaro Velasquez +1
This paper presents a quantum algorithm for efficiently decoding hypervectors, a crucial process in extracting atomic elements from hypervectors - an essential task in Hyperdimensi…
Generalized Holographic Reduced Representations
Calvin Yeung, Zhuowen Zou, SungHeon Jeong +3
Hyperdimensional Computing (HDC) is a computationally and data-efficient paradigm that acts as a bridge between connectionist and symbolic approaches to artificial intelligence (AI…
Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures
Calvin Yeung, Prathyush Poduval, Mohsen Imani
Vector Symbolic Architectures (VSAs) have emerged as a novel framework for enabling interpretable machine learning algorithms equipped with the ability to reason and explain their…
Learning from Hypervectors: A Survey on Hyperdimensional Encoding
Sercan Aygun, Mehran Shoushtari Moghadam, M. Hassan Najafi +1
Hyperdimensional computing (HDC) is an emerging computing paradigm that imitates the brain's structure to offer a powerful and efficient processing and learning model. In HDC, the…