20 citations · 36 across the 4 of their papers we have counts for
6 papers
Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction
Yipeng Li, Ashutosh Hathidara, Jane Lo +3
Tool-calling agents have become central to enterprise AI, yet training and evaluating them at scale remains severely constrained due to business and legal restrictions on enterpris…
EDGEGEN: Improving Tool-Calling Agents Beyond Happy Paths with Synthetic Edge Case Generation
Harshavardhan Abichandani, Penny Chong, Jiyuan Shen +7
Tool-calling LLM agents are increasingly deployed in enterprise applications. However, effective evaluation and optimization require high-quality, diverse task datasets that are of…
Optimization before Evaluation: Evaluation with Unoptimised Prompts Can be Misleading
Nicholas Sadjoli, Tim Siefken, Atin Ghosh +2
Current Large Language Model (LLM) evaluation frameworks utilize the same static prompt template across all models under evaluation. This differs from the common industry practice…
Pretrained equivariant features improve unsupervised landmark discovery
Rahul Rahaman, Atin Ghosh, Alexandre H. Thiery
Locating semantically meaningful landmark points is a crucial component of a large number of computer vision pipelines. Because of the small number of available datasets with groun…
On Data-Augmentation and Consistency-Based Semi-Supervised Learning
Atin Ghosh, Alexandre H. Thiery
Recently proposed consistency-based Semi-Supervised Learning (SSL) methods such as the -model, temporal ensembling, the mean teacher, or the virtual adversarial training, have a…
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting
Srayanta Mukherjee, Devashish Shankar, Atin Ghosh +4
Accurate demand forecasts can help on-line retail organizations better plan their supply-chain processes. The challenge, however, is the large number of associative factors that re…