12 citations · 12 across the 16 of their papers we have counts for
9 papers · 1 filter
EulerLoRA: Rank-Driven Jump Dynamics for Calibrated Parameter-Efficient Fine-Tuning
Srinivas Anumasa, Dianbo Liu
Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning, but standard LoRA produces a single deterministic model and does not directly support predictive uncertainty est…
Breaking Diversity Collapse in Spiking Pseudo-Ensembles for Efficient OOD Detection in Remote Sensing
Srinivas Anumasa, Rushi Shah, Qiran Zou +1
Spiking Neural Networks (SNNs) are attractive for resource-constrained remote-sensing systems, but reliable out-of-distribution (OOD) detection remains challenging. Deep ensembles…
FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics
Qiran Zou, Hou Hei Lam, Wenhao Zhao +11
AI research agents accelerate ML research by automating hypothesis generation, experimentation, and empirical refinement. Existing agent strategies range from greedy hill-climbing…
Data-Dependent Smoothing for Protein Discovery with Walk-Jump Sampling
Srinivas Anumasa, Barath Chandran. C, Tingting Chen +1
Diffusion models have emerged as a powerful class of generative models by learning to iteratively reverse the noising process. Their ability to generate high-quality samples has ex…
Auto-Discovery-Bench: Diagnosing Structured State Tracking in Oracle-Guided Discovery
Tingting Chen, Beibei Lin, Srinivas Anumasa +5
Interactive discovery requires agents to maintain and update structured beliefs over many rounds of feedback. Before evaluating agents in noisy, open-ended scientific environments,…
Continuous Depth Recurrent Neural Differential Equations
Srinivas Anumasa, Geetakrishnasai Gunapati, P. K. Srijith
Recurrent neural networks (RNNs) have brought a lot of advancements in sequence labeling tasks and sequence data. However, their effectiveness is limited when the observations in t…