6 citations · 21 across the 34 of their papers we have counts for
10 papers · 1 filter
FAME: FPGA Acceleration of Secure Matrix Multiplication with Homomorphic Encryption
Zhihan Xu, Rajgopal Kannan, Viktor K. Prasanna
Homomorphic Encryption (HE) enables secure computation on encrypted data, addressing privacy concerns in cloud computing. However, the high computational cost of HE operations, par…
LYNX: Learning Dynamic Exits for Confidence-Controlled Reasoning
Ömer Faruk Akgül, Yusuf Hakan Kalaycı, Rajgopal Kannan +2
Large reasoning models achieve strong performance on complex tasks by generating extended chains of thought, but they often "overthink": continuing to reason long after they have e…
HierRouter: Coordinated Routing of Specialized Large Language Models via Reinforcement Learning
Nikunj Gupta, Bill Guo, Rajgopal Kannan +1
Large Language Models (LLMs) deliver state-of-the-art performance across many tasks but impose high computational and memory costs, limiting their deployment in resource-constraine…
TIGER-MARL: Enhancing Multi-Agent Reinforcement Learning with Temporal Information through Graph-based Embeddings and Representations
Nikunj Gupta, Ludwika Twardecka, James Zachary Hare +3
In this paper, we propose capturing and utilizing \textit{Temporal Information through Graph-based Embeddings and Representations} or \textbf{TIGER} to enhance multi-agent reinforc…
Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say
Jacob Fein-Ashley, Dhruv Parikh, Rajgopal Kannan +1
Open-source Large Language Models (LLMs) increasingly specialize by domain (e.g., math, code, general reasoning), motivating systems that leverage complementary strengths across mo…
AMPED: Accelerating MTTKRP for Billion-Scale Sparse Tensor Decomposition on Multiple GPUs
Sasindu Wijeratne, Rajgopal Kannan, Viktor Prasanna
Matricized Tensor Times Khatri-Rao Product (MTTKRP) is the computational bottleneck in sparse tensor decomposition. As real-world sparse tensors grow to billions of nonzeros, they…
RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion
Ömer Faruk Akgül, Feiyu Zhu, Yuxin Yang +2
Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities. TKG completion involves forecasting missing or future links, requiring models to…
Adversarial Knapsack for Sequential Competitive Resource Allocation
Omkar Thakoor, Rajgopal Kannan, Victor Prasanna
This work addresses competitive resource allocation in a sequential setting, where two players allocate resources across objects or locations of shared interest. Departing from the…
Deep Meta Coordination Graphs for Multi-agent Reinforcement Learning
Nikunj Gupta, James Zachary Hare, Jesse Milzman +2
This paper presents deep meta coordination graphs (DMCG) for learning cooperative policies in multi-agent reinforcement learning (MARL). Coordination graph formulations encode loca…
ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning
Dhruv Parikh, Jacob Fein-Ashley, Tian Ye +2
Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have dominated the field of Computer Vision (CV). Graph Neural Networks (GNN) have performed remarkably well acros…