Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing

no code implementations • 22 Apr 2024 • Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, Victor Ruhle, Laks V. S. Lakshmanan, Ahmed Hassan Awadallah

Large language models (LLMs) excel in most NLP tasks but also require expensive cloud servers for deployment due to their size, while smaller models that can be deployed on lower cost (e. g., edge) devices, tend to lag behind in terms of response quality.

Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation

no code implementations • NeurIPS 2021 • Divyansh Jhunjhunwala, Ankur Mallick, Advait Gadhikar, Swanand Kadhe, Gauri Joshi

We study the problem of estimating at a central server the mean of a set of vectors distributed across several nodes (one vector per node).

Federated Learning

Probabilistic Neighbourhood Component Analysis: Sample Efficient Uncertainty Estimation in Deep Learning

1 code implementation • 18 Jul 2020 • Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, T. Yong-Jin Han

In this work, we show that the uncertainty estimation capability of state-of-the-art BNNs and Deep Ensemble models degrades significantly when the amount of training data is small.

COVID-19 Diagnosis Uncertainty Quantification

Deep Kernels with Probabilistic Embeddings for Small-Data Learning

1 code implementation • 13 Oct 2019 • Ankur Mallick, Chaitanya Dwivedi, Bhavya Kailkhura, Gauri Joshi, T. Yong-Jin Han

Experiments on a variety of datasets show that our approach outperforms the state-of-the-art in GP kernel learning in both supervised and semi-supervised settings.

Gaussian Processes Representation Learning +1