112 citations · 119 across the 8 of their papers we have counts for
9 papers
Memory as Middleware for Self-Improving AI Agents
K. R. Jayaram, Vatche Isahagian, Vinod Muthusamy +4
AI agents are stateless across sessions by default and therefore operationally amnesic: each session begins with little durable knowledge of prior failures, repairs, preferences, o…
Trajectory-Informed Memory Generation for Self-Improving Agent Systems
Gaodan Fang, Vatche Isahagian, K. R. Jayaram +4
LLM-powered agents face a persistent challenge: learning from their execution experiences to improve future performance. While agents can successfully complete many tasks, they oft…
FLOW-BENCH: Towards Conversational Generation of Enterprise Workflows
Evelyn Duesterwald, Siyu Huo, Vatche Isahagian +7
Business process automation (BPA) that leverages Large Language Models (LLMs) to convert natural language (NL) instructions into structured business process artifacts is becoming a…
FLIPS: Federated Learning using Intelligent Participant Selection
Rahul Atul Bhope, K. R. Jayaram, Nalini Venkatasubramanian +2
This paper presents the design and implementation of FLIPS, a middleware system to manage data and participant heterogeneity in federated learning (FL) training workloads. In parti…
Just-in-Time Aggregation for Federated Learning
K. R. Jayaram, Ashish Verma, Gegi Thomas +1
The increasing number and scale of federated learning (FL) jobs necessitates resource efficient scheduling and management of aggregation to make the economics of cloud-hosted aggre…
Adaptive Aggregation For Federated Learning
K. R. Jayaram, Vinod Muthusamy, Gegi Thomas +2
Advances in federated learning (FL) algorithms,along with technologies like differential privacy and homomorphic encryption, have led to FL being increasingly adopted and used in m…
MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent
K. R. Jayaram, Archit Verma, Ashish Verma +2
Federated learning enables multiple, distributed participants (potentially on different clouds) to collaborate and train machine/deep learning models by sharing parameters/gradient…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Atin Sood, Benjamin Elder, Benjamin Herta +17
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through AP…