Search Results for author:
Found 7 papers, 2 papers with code
Brain-to-Text Benchmark '24: Lessons Learned
Speech brain-computer interfaces aim to decipher what a person is trying to say from neural activity alone, restoring communication to people with paralysis who have lost the ability to speak intelligibly.
Switching Autoregressive Low-rank Tensor Models
In contrast, SLDSs can capture long-range dependencies in a parameter efficient way through Markovian latent dynamics, but present an intractable likelihood and a challenging parameter estimation task.
Three-dimensional spike localization and improved motion correction for Neuropixels recordings
Neuropixels (NP) probes are dense linear multi-electrode arrays that have rapidly become essential tools for studying the electrophysiology of large neural populations.
Intent-based Product Collections for E-commerce using Pretrained Language Models
In this paper, we use a pretrained language model (PLM) that leverages textual attributes of web-scale products to make intent-based product collections.
FALCON: Fast and Lightweight Convolution for Compressing and Accelerating CNN
A promising direction is based on depthwise separable convolution which replaces a standard convolution with a depthwise convolution and a pointwise convolution.
FALCON: Lightweight and Accurate Convolution
By exploiting the knowledge of a trained standard model and carefully determining the order of depthwise separable convolution via GEP, FALCON achieves sufficient accuracy close to that of the trained standard model.
