170 citations · 210 across the 11 of their papers we have counts for
12 papers
The AI Neuroscientist: An Interactive Agentic Interface for Neuroimaging Analysis
Aakash Patel, Panos Ketonis, Shreya Saxena +2
Analyzing neuroimaging data requires specialized coding and statistical expertise, which limits accessibility for researchers without computational backgrounds. We present the AI N…
FLUX: Geometry-Aware Longitudinal Flow Matching with Mixture of Experts
Josue Ortega Caro, Yongxu Zhang, Hannah M Batchelor +3
Many biological systems evolve through continuous local dynamics while switching between latent regimes defined by learning, stimulus context, internal state, or developmental stag…
Sparse-IFT: Sparse Iso-FLOP Transformations for Maximizing Training Efficiency
Vithursan Thangarasa, Shreyas Saxena, Abhay Gupta +1
Recent research has focused on weight sparsity in deep neural network training to reduce FLOPs, aiming for improved efficiency (test accuracy w.r.t training FLOPs). However, sparse…
SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models
Vithursan Thangarasa, Abhay Gupta, William Marshall +5
The pre-training and fine-tuning paradigm has contributed to a number of breakthroughs in Natural Language Processing (NLP). Instead of directly training on a downstream task, lang…
Instance-Level Task Parameters: A Robust Multi-task Weighting Framework
Pavan Kumar Anasosalu Vasu, Shreyas Saxena, Oncel Tuzel
Recent works have shown that deep neural networks benefit from multi-task learning by learning a shared representation across several related tasks. However, performance of such sy…
Training With Data Dependent Dynamic Learning Rates
Shreyas Saxena, Nidhi Vyas, Dennis DeCoste
Recently many first and second order variants of SGD have been proposed to facilitate training of Deep Neural Networks (DNNs). A common limitation of these works stem from the fact…
Dynamic curriculum learning via data parameters for noise robust keyword spotting
Takuya Higuchi, Shreyas Saxena, Mehrez Souden +3
We propose dynamic curriculum learning via data parameters for noise robust keyword spotting. Data parameter learning has recently been introduced for image processing, where weigh…
Chronic, cortex-wide imaging of specific cell populations during behavior
Joao Couto, Simon Musall, Xiaonan R Sun +8
Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes like attention, decision-making, and act…
Learning Soft Labels via Meta Learning
Nidhi Vyas, Shreyas Saxena, Thomas Voice
One-hot labels do not represent soft decision boundaries among concepts, and hence, models trained on them are prone to overfitting. Using soft labels as targets provide regulariza…
Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Data
Daniel Hernandez, Antonio Khalil Moretti, Ziqiang Wei +3
Latent variable models have been widely applied for the analysis of time series resulting from experimental neuroscience techniques. In these datasets, observations are relatively…
Learning Unsupervised Visual Grounding Through Semantic Self-Supervision
Syed Ashar Javed, Shreyas Saxena, Vineet Gandhi
Localizing natural language phrases in images is a challenging problem that requires joint understanding of both the textual and visual modalities. In the unsupervised setting, lac…
Convolutional Neural Fabrics
Shreyas Saxena, Jakob Verbeek
Despite the success of CNNs, selecting the optimal architecture for a given task remains an open problem. Instead of aiming to select a single optimal architecture, we propose a "f…