no code implementations • 28 Oct 2023 • Hailin Chen, Amrita Saha, Steven Hoi, Shafiq Joty
With the rise of powerful closed-sourced LLMs (ChatGPT, GPT-4), there are increasing interests in distilling the capabilies of close-sourced LLMs to smaller open-sourced LLMs.
1 code implementation • 13 Oct 2023 • Hung Le, Hailin Chen, Amrita Saha, Akash Gokul, Doyen Sahoo, Shafiq Joty
We find that by naturally encouraging the LLM to reuse the previously developed and verified sub-modules, CodeChain can significantly boost both modularity as well as correctness of the generated solutions, achieving relative pass@1 improvements of 35% on APPS and 76% on CodeContests.
no code implementations • 10 Apr 2023 • Qian Cheng, Doyen Sahoo, Amrita Saha, Wenzhuo Yang, Chenghao Liu, Gerald Woo, Manpreet Singh, Silvio Saverese, Steven C. H. Hoi
There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency.
1 code implementation • 31 Jan 2023 • Qian Cheng, Amrita Saha, Wenzhuo Yang, Chenghao Liu, Doyen Sahoo, Steven Hoi
In order to enable users to perform multiple types of AI-based log analysis tasks in a uniform manner, we introduce LogAI (https://github. com/salesforce/logai), a one-stop open source library for log analytics and intelligence.
1 code implementation • 30 Nov 2022 • Hailin Chen, Amrita Saha, Shafiq Joty, Steven C. H. Hoi
Machine learning models usually assume i. i. d data during training and testing, but data and tasks in real world often change over time.
1 code implementation • 23 May 2022 • Rishabh Bhardwaj, Amrita Saha, Steven C. H. Hoi, Soujanya Poria
VIP particularly focuses on two aspects -- contextual prompts that learns input-specific contextualization of the soft prompt tokens through a small-scale sentence encoder and quantized prompts that maps the contextualized prompts to a set of learnable codebook vectors through a Vector quantization network.
no code implementations • 21 Apr 2022 • Amrita Saha, Steven C. H. Hoi
ICA forms the backbone of a simple-yet-effective Retrieval based RCA for new incidents, through an Information Retrieval system to search and rank past incidents and detect likely root causes from them, given the incident symptom.
1 code implementation • 20 Sep 2021 • Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K C Krithika, Sukumar Maddineni, Daeki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven Hoi, Huan Wang
We introduce Merlion, an open-source machine learning library for time series.
no code implementations • 28 Jan 2021 • Amrita Saha, Shafiq Joty, Steven C. H. Hoi
Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic form of numerical reasoning over text in Machine Reading Comprehension (MRC).
no code implementations • 3 Nov 2019 • Sahana Ramnath, Amrita Saha, Soumen Chakrabarti, Mitesh M. Khapra
With the prolification of multimodal interaction in various domains, recently there has been much interest in text based image retrieval in the computer vision community.
no code implementations • TACL 2019 • Amrita Saha, Ghulam Ahmed Ansari, Abhishek Laddha, Karthik Sankaranarayanan, Soumen Chakrabarti
On one of the hardest class of programs (comparative reasoning) with 5{--}10 steps, CIPITR outperforms NSM by a factor of 89 and memory networks by 9 times.
1 code implementation • ACL 2018 • Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, Karthik Sankaranarayanan
We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets.
1 code implementation • 31 Jan 2018 • Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra, Karthik Sankaranarayanan, Sarath Chandar
Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in our dialogs require a larger subgraph of the KG.
no code implementations • EACL 2017 • Roy Bar-Haim, Indrajit Bhattacharya, Francesco Dinuzzo, Amrita Saha, Noam Slonim
Recent work has addressed the problem of detecting relevant claims for a given controversial topic.
no code implementations • 1 Apr 2017 • Amrita Saha, Mitesh Khapra, Karthik Sankaranarayanan
With this dataset, we propose 5 new sub-tasks for multimodal conversations along with their evaluation methodology.
no code implementations • COLING 2016 • Amrita Saha, Mitesh M. Khapra, Sarath Chandar, Janarthanan Rajendran, Kyunghyun Cho
However, there is no parallel training data available between X and Y but, training data is available between X & Z and Z & Y (as is often the case in many real world applications).
no code implementations • 1 Dec 2015 • Amrita Saha, Sathish Indurthi, Shantanu Godbole, Subendhu Rongali, Vikas C. Raykar
We describe the problem of aggregating the label predictions of diverse classifiers using a class taxonomy.
no code implementations • COLING 2014 • Noam Slonim, Ehud Aharoni, Carlos Alzate, Roy Bar-Haim, Yonatan Bilu, Lena Dankin, Iris Eiron, Daniel Hershcovich, Shay Hummel, Mitesh Khapra, Tamar Lavee, Ran Levy, Paul Matchen, Anatoly Polnarov, Vikas Raykar, Ruty Rinott, Amrita Saha, Naama Zwerdling, David Konopnicki, Dan Gutfreund
no code implementations • NeurIPS 2014 • Sarath Chandar A P, Stanislas Lauly, Hugo Larochelle, Mitesh M. Khapra, Balaraman Ravindran, Vikas Raykar, Amrita Saha
Cross-language learning allows us to use training data from one language to build models for a different language.