Search Results for author: Kartik Talamadupula

Found 39 papers, 6 papers with code

Are Human Conversations Special? A Large Language Model Perspective

no code implementations8 Mar 2024 Toshish Jawale, Chaitanya Animesh, Sekhar Vallath, Kartik Talamadupula, Larry Heck

This study analyzes changes in the attention mechanisms of large language models (LLMs) when used to understand natural conversations between humans (human-human).

Language Modelling Large Language Model

Biomechanics-Guided Facial Action Unit Detection Through Force Modeling

no code implementations CVPR 2023 Zijun Cui, Chenyi Kuang, Tian Gao, Kartik Talamadupula, Qiang Ji

In this paper, we propose a biomechanics-guided AU detection approach, where facial muscle activation forces are modelled, and are employed to predict AU activation.

Action Unit Detection Facial Action Unit Detection

Knowledge-augmented Deep Learning and Its Applications: A Survey

no code implementations30 Nov 2022 Zijun Cui, Tian Gao, Kartik Talamadupula, Qiang Ji

Based on our taxonomy, we provide a systematic review of existing techniques, different from existing works that survey integration approaches agnostic to taxonomy of knowledge.

Investigating Explainability of Generative AI for Code through Scenario-based Design

no code implementations10 Feb 2022 Jiao Sun, Q. Vera Liao, Michael Muller, Mayank Agarwal, Stephanie Houde, Kartik Talamadupula, Justin D. Weisz

Using scenario-based design and question-driven XAI design approaches, we explore users' explainability needs for GenAI in three software engineering use cases: natural language to code, code translation, and code auto-completion.

Code Translation Explainable Artificial Intelligence (XAI)

Using Document Similarity Methods to create Parallel Datasets for Code Translation

no code implementations11 Oct 2021 Mayank Agarwal, Kartik Talamadupula, Fernando Martinez, Stephanie Houde, Michael Muller, John Richards, Steven I Ross, Justin D. Weisz

However, due to the paucity of parallel data in this domain, supervised techniques have only been applied to a limited set of popular programming languages.

Code Translation Machine Translation +1

Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agents

no code implementations9 Jun 2021 Keerthiram Murugesan, Subhajit Chaudhury, Kartik Talamadupula

This improves the agent's overall understanding of the game 'scene' and objects' relationships to the world around them, and the variety of visual representations on offer allow the agent to generate a better generalization of a relationship.

reinforcement-learning Reinforcement Learning (RL) +2

Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization

no code implementations NAACL 2021 Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, Kartik Talamadupula

Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled.

Multiple-choice Natural Language Inference +4

NeurIPS 2020 NLC2CMD Competition: Translating Natural Language to Bash Commands

1 code implementation3 Mar 2021 Mayank Agarwal, Tathagata Chakraborti, Quchen Fu, David Gros, Xi Victoria Lin, Jaron Maene, Kartik Talamadupula, Zhongwei Teng, Jules White

The NLC2CMD Competition hosted at NeurIPS 2020 aimed to bring the power of natural language processing to the command line.

VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning

no code implementations26 Oct 2020 Thomas Carta, Subhajit Chaudhury, Kartik Talamadupula, Michiaki Tatsubori

The goal is to force an RL agent to use both text and visual features to predict natural language action commands for solving the final task of cooking a meal.

Atari Games reinforcement-learning +2

Reading Comprehension as Natural Language Inference: A Semantic Analysis

no code implementations4 Oct 2020 Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula

We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.

Natural Language Inference Question Answering +1

Bootstrapped Q-learning with Context Relevant Observation Pruning to Generalize in Text-based Games

1 code implementation EMNLP 2020 Subhajit Chaudhury, Daiki Kimura, Kartik Talamadupula, Michiaki Tatsubori, Asim Munawar, Ryuki Tachibana

Our bootstrapped agent shows improved generalization in solving unseen TextWorld games, using 10x-20x fewer training games compared to previous state-of-the-art methods despite requiring less number of training episodes.

Q-Learning Reinforcement Learning (RL) +1

Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks

no code implementations18 Sep 2020 Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Li, Pavan Kapanipathi, Kartik Talamadupula

In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it.

Natural Language Inference Question Answering +2

Type-augmented Relation Prediction in Knowledge Graphs

no code implementations16 Sep 2020 Zijun Cui, Pavan Kapanipathi, Kartik Talamadupula, Tian Gao, Qiang Ji

Knowledge graph completion (also known as relation prediction) is the task of inferring missing facts given existing ones.

Relation Vocal Bursts Type Prediction

Towards an Atlas of Cultural Commonsense for Machine Reasoning

no code implementations11 Sep 2020 Anurag Acharya, Kartik Talamadupula, Mark A. Finlayson

Existing commonsense reasoning datasets for AI and NLP tasks fail to address an important aspect of human life: cultural differences.

Question Answering

Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Approaches

no code implementations12 Jul 2020 Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell

We introduce a number of RL agents that combine the sequential context with a dynamic graph representation of their beliefs of the world and commonsense knowledge from ConceptNet in different ways.

Decision Making Reinforcement Learning (RL) +1

Enhancing Text-based Reinforcement Learning Agents with Commonsense Knowledge

no code implementations2 May 2020 Keerthiram Murugesan, Mattia Atzeni, Pushkar Shukla, Mrinmaya Sachan, Pavan Kapanipathi, Kartik Talamadupula

In this paper, we consider the recent trend of evaluating progress on reinforcement learning technology by using text-based environments and games as evaluation environments.

reinforcement-learning Reinforcement Learning (RL)

Project CLAI: Instrumenting the Command Line as a New Environment for AI Agents

1 code implementation31 Jan 2020 Mayank Agarwal, Jorge J. Barroso, Tathagata Chakraborti, Eli M. Dow, Kshitij Fadnis, Borja Godoy, Madhavan Pallan, Kartik Talamadupula

This whitepaper reports on Project CLAI (Command Line AI), which aims to bring the power of AI to the command line interface (CLI).

Path-Based Contextualization of Knowledge Graphs for Textual Entailment

no code implementations5 Nov 2019 Kshitij Fadnis, Kartik Talamadupula, Pavan Kapanipathi, Haque Ishfaq, Salim Roukos, Achille Fokoue

In this paper, we introduce the problem of knowledge graph contextualization -- that is, given a specific NLP task, the problem of extracting meaningful and relevant sub-graphs from a given knowledge graph.

Knowledge Graphs Natural Language Inference

Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks

no code implementations5 Nov 2019 Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue

A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.

Knowledge Graphs Natural Language Inference

Tentacular Artificial Intelligence, and the Architecture Thereof, Introduced

no code implementations14 Oct 2018 Selmer Bringsjord, Naveen Sundar Govindarajulu, Atriya Sen, Matthew Peveler, Biplav Srivastava, Kartik Talamadupula

We briefly introduce herein a new form of distributed, multi-agent artificial intelligence, which we refer to as "tentacular."

Answering Science Exam Questions Using Query Rewriting with Background Knowledge

no code implementations15 Sep 2018 Ryan Musa, Xiaoyan Wang, Achille Fokoue, Nicholas Mattei, Maria Chang, Pavan Kapanipathi, Bassem Makni, Kartik Talamadupula, Michael Witbrock

Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.

Information Retrieval Multiple-choice +3

Toward Cognitive and Immersive Systems: Experiments in a Cognitive Microworld

no code implementations14 Sep 2017 Matthew Peveler, Naveen Sundar Govindarajulu, Selmer Bringsjord, Biplav Srivastava, Kartik Talamadupula, Hui Su

These \textit{cognitive and immersive systems} (CAISs) fall squarely into the intersection of AI with HCI/HRI: such systems interact with and assist the human agents that enter them, in no small part because such systems are infused with AI able to understand and reason about these humans and their knowledge, beliefs, goals, communications, plans, etc.

Workflow Complexity for Collaborative Interactions: Where are the Metrics? -- A Challenge

no code implementations13 Sep 2017 Kartik Talamadupula, Biplav Srivastava, Jeffrey O. Kephart

In this paper, we introduce the problem of denoting and deriving the complexity of workflows (plans, schedules) in collaborative, planner-assisted settings where humans and agents are trying to jointly solve a task.

Visualizations for an Explainable Planning Agent

no code implementations13 Sep 2017 Tathagata Chakraborti, Kshitij P. Fadnis, Kartik Talamadupula, Mishal Dholakia, Biplav Srivastava, Jeffrey O. Kephart, Rachel K. E. Bellamy

In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making.

Decision Making

UbuntuWorld 1.0 LTS - A Platform for Automated Problem Solving & Troubleshooting in the Ubuntu OS

no code implementations27 Sep 2016 Tathagata Chakraborti, Kartik Talamadupula, Kshitij P. Fadnis, Murray Campbell, Subbarao Kambhampati

In this paper, we present UbuntuWorld 1. 0 LTS - a platform for developing automated technical support agents in the Ubuntu operating system.

Reinforcement Learning (RL)

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation

4 code implementations2 Jun 2016 Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bo-Wen Zhou, Yoshua Bengio, Aaron Courville

We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens.

Dialogue Generation Response Generation

The Metrics Matter! On the Incompatibility of Different Flavors of Replanning

no code implementations12 May 2014 Kartik Talamadupula, David E. Smith, Subbarao Kambhampati

An open question is whether these metrics are interchangeable; answering this requires a normalized comparison of the various replanning quality metrics.

Open-Ended Question Answering

Herding the Crowd: Automated Planning for Crowdsourced Planning

no code implementations29 Jul 2013 Kartik Talamadupula, Subbarao Kambhampati

In this paper, we will argue that the automated oversight used in these systems can be viewed as a primitive automated planner, and that there are several opportunities for more sophisticated automated planning in effectively steering crowdsourced planning.


Strategic Planning for Network Data Analysis

no code implementations12 May 2013 Kartik Talamadupula, Octavian Udrea, Anton Riabov, Anand Ranganathan

In this paper, we motivate the novel "strategic planning" problem -- one of gathering data from the world and applying the underlying model of the domain in order to come up with decisions that will monitor the system in an automated manner.

Malware Detection

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