7 citations · 12 across the 10 of their papers we have counts for
9 papers · 1 filter
Heterogeneous Decentralized Diffusion Models
Zhiying Jiang, Raihan Seraj, Marcos Villagra +1
Training frontier-scale diffusion models often requires substantial computational resources concentrated in tightly-coupled clusters, limiting participation to well-resourced insti…
Expert-Data Alignment Governs Generation Quality in Decentralized Diffusion Models
Marcos Villagra, Bidhan Roy, Raihan Seraj +1
Decentralized Diffusion Models (DDMs) route denoising through experts trained independently on disjoint data clusters, which can strongly disagree in their predictions. What govern…
Generalizing Multi-Step Inverse Models for Representation Learning to Finite-Memory POMDPs
Lili Wu, Ben Evans, Riashat Islam +3
Discovering an informative, or agent-centric, state representation that encodes only the relevant information while discarding the irrelevant is a key challenge towards scaling rei…
PcLast: Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen +11
Goal-conditioned planning benefits from learned low-dimensional representations of rich observations. While compact latent representations typically learned from variational autoen…
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
Qi Yan, Raihan Seraj, Jiawei He +2
Machine-based prediction of real-world events is garnering attention due to its potential for informed decision-making. Whereas traditional forecasting predominantly hinges on stru…
Tsetlin Machine for Solving Contextual Bandit Problems
Raihan Seraj, Jivitesh Sharma, Ole-Christoffer Granmo
This paper introduces an interpretable contextual bandit algorithm using Tsetlin Machines, which solves complex pattern recognition tasks using propositional logic. The proposed ba…