3 citations · 6 across the 6 of their papers we have counts for
6 papers
Causal Foundation Models
Christopher Stith, Hossein Rahmani, Jesse C. Cresswell
Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first propos…
AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios
Lisa Alazraki, Lihu Chen, Ana Brassard +3
Large Language Models (LLMs) have achieved high accuracy on complex commonsense and mathematical problems that involve the composition of multiple reasoning steps. However, current…
Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots
Vincent Guan, Joseph Janssen, Hossein Rahmani +4
Stochastic differential equations (SDEs) are a fundamental tool for modelling dynamic processes, including gene regulatory networks (GRNs), contaminant transport, financial markets…
Towards Adaptive Pseudo-label Learning for Semi-Supervised Temporal Action Localization
Feixiang Zhou, Bryan Williams, Hossein Rahmani
Alleviating noisy pseudo labels remains a key challenge in Semi-Supervised Temporal Action Localization (SS-TAL). Existing methods often filter pseudo labels based on strict condit…
Weakly Supervised Co-training with Swapping Assignments for Semantic Segmentation
Xinyu Yang, Hossein Rahmani, Sue Black +1
Class activation maps (CAMs) are commonly employed in weakly supervised semantic segmentation (WSSS) to produce pseudo-labels. Due to incomplete or excessive class activation, exis…
3D Points Splatting for Real-Time Dynamic Hand Reconstruction
Zheheng Jiang, Hossein Rahmani, Sue Black +1
We present 3D Points Splatting Hand Reconstruction (3D-PSHR), a real-time and photo-realistic hand reconstruction approach. We propose a self-adaptive canonical points upsampling s…