#deep learning
82 papers · 1 filter
Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments
Roy Shivam Ram Shreshtth, Arnab Hazra, Gourab Mukherjee
The paper examines how neural network architectures such as feedforward nets, Deep Sets, and Transformers can be used to amortize Bayesian inference, providing fast approximate pos…
Cortical-SSM: A Deep State Space Model for Motor Imagery Decoding from EEG Signals
Shuntaro Suzuki, Shunya Nagashima, Komei Sugiura
The paper introduces Cortical-SSM, a deep state space model that decodes motor imagery EEG signals by integrating temporal, spatial, and frequency information, achieving higher acc…
EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface
Bipul Thapa, Biplov Paneru, Bishwash Paneru +1
The paper proposes a Transformer‑based deep learning model (TFormerEEG) to classify motor‑imagery EEG signals for controlling a simulated wheelchair, achieving over 90% accuracy.
Uniform Approximation of Functions with Asymmetric Growth and Decay by Deep Weighted Polynomials
Kingsley Yeon, Steven B. Damelin
The paper proposes a class of weighted deep (composite) polynomials that can uniformly approximate functions which grow on one side of the real line and decay on the other, and dem…
Deep-learning Causal Retrieval Optimization for Efficient e-commerce Distribution in Pinterest
Junpeng Hou, XianXing Zhang, Sai Xiao +6
The paper presents a deep learning system that decides when to trigger shopping recommendations in Pinterest, using causal inference to reduce unnecessary triggers while maintainin…
Reassessing Muon for Matrix Factorization
Ali Parviz, Gal Mishne, Alex Cloninger
The paper investigates the Muon optimizer on a simple low‑rank matrix factorization task, comparing it to carefully tuned AdamW baselines, and finds that Muon does not consistently…