Decision Making under Uncertainty with Convolutional Deep Gaussian Processes. - Dinesh Jain; Srinivas Anumasa; P. K. Srijith (COMAD/CODS, 2020)

Delay Differential Neural Networks. - Srinivas Anumasa; P. K. Srijith (ICMLT, 2021)

Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations. - Srinivas Anumasa; P. K. Srijith (WACV, 2021)

Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification. - Maunika Tamire; Srinivas Anumasa; P. K. Srijith (CoRR, 2021)

Latent Time Neural Ordinary Differential Equations. - Srinivas Anumasa; P. K. Srijith (AAAI, 2022)

A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates. - Haiyan Jiang; Srinivas Anumasa; Giulia De Masi; Huan Xiong; Bin Gu 001 (ICML, 2023)

Continuous Depth Recurrent Neural Differential Equations. - Srinivas Anumasa; Geetakrishnasai Gunapati; P. K. Srijith (ECML/PKDD (2), 2023)

Enhancing Training of Spiking Neural Network with Stochastic Latency. - Srinivas Anumasa; Bhaskar Mukhoty; Velibor Bojkovic; Giulia De Masi; Huan Xiong; Bin Gu 001 (AAAI, 2024)

Data Driven Threshold and Potential Initialization for Spiking Neural Networks. - Velibor Bojkovic; Srinivas Anumasa; Giulia De Masi; Bin Gu 001; Huan Xiong (AISTATS, 2024)

Multi-Novelty: Improve the Diversity and Novelty of Contents Generated by Large Language Models via inference-time Multi-Views Brainstorming. - Arash Lagzian; Srinivas Anumasa; Dianbo Liu (CoRR, 2025)

Auto-Bench: An Automated Benchmark for Scientific Discovery in LLMs. - Tingting Chen; Srinivas Anumasa; Beibei Lin; Vedant Shah; Anirudh Goyal; Dianbo Liu (CoRR, 2025)