Search Results for author: Adarsh Pyarelal

Found 11 papers, 4 papers with code

Rule Based Event Extraction for Artificial Social Intelligence

no code implementations PANDL (COLING) 2022 Remo Nitschke, Yuwei Wang, Chen Chen, Adarsh Pyarelal, Rebecca Sharp

Natural language (as opposed to structured communication modes such as Morse code) is by far the most common mode of communication between humans, and can thus provide significant insight into both individual mental states and interpersonal dynamics.

Event Extraction

Hierarchical Fusion for Online Multimodal Dialog Act Classification

1 code implementation EMNLP 2023 Md Messal Monem Miah, Adarsh Pyarelal, Ruihong Huang

We propose a framework for online multimodal dialog act (DA) classification based on raw audio and ASR-generated transcriptions of current and past utterances.

Classification Dialog Act Classification +1

Probabilistic Modeling of Human Teams to Infer False Beliefs

no code implementations19 Oct 2023 Paulo Soares, Adarsh Pyarelal, Kobus Barnard

We find that the players' behaviors are affected by what they see in their in-game field of view, their beliefs about the meaning of the markers, and their beliefs about which meaning the team decided to adopt.

Multi-Timescale Modeling of Human Behavior

no code implementations16 Nov 2022 Chinmai Basavaraj, Adarsh Pyarelal, Evan Carter

We demonstrate that our approach for modeling behavior in multiple timescales substantially improves prediction of future behavior compared to methods that do not model behavior at multiple timescales.


Deep Reinforcement Learning with Vector Quantized Encoding

no code implementations12 Nov 2022 Liang Zhang, Justin Lieffers, Adarsh Pyarelal

Human decision-making often involves combining similar states into categories and reasoning at the level of the categories rather than the actual states.

Decision Making reinforcement-learning +1

Using Features at Multiple Temporal and Spatial Resolutions to Predict Human Behavior in Real Time

no code implementations12 Nov 2022 Liang Zhang, Justin Lieffers, Adarsh Pyarelal

We contend that for an artificially intelligent agent to effectively model human teammates, i. e., demonstrate computational theory of mind (ToM), it should do the same.

Modular Procedural Generation for Voxel Maps

no code implementations18 Apr 2021 Adarsh Pyarelal, Aditya Banerjee, Kobus Barnard

The benefits of this approach include rapid, scalable, and efficient development of virtual environments, the ability to control the statistics of the environment at a semantic level, and the ability to generate novel environments in response to player actions in real time.

MathAlign: Linking Formula Identifiers to their Contextual Natural Language Descriptions

no code implementations LREC 2020 Maria Alexeeva, Rebecca Sharp, Marco A. Valenzuela-Esc{\'a}rcega, Jennifer Kadowaki, Adarsh Pyarelal, Clayton Morrison

Extending machine reading approaches to extract mathematical concepts and their descriptions is useful for a variety of tasks, ranging from mathematical information retrieval to increasing accessibility of scientific documents for the visually impaired.

Information Retrieval Reading Comprehension +1

AutoMATES: Automated Model Assembly from Text, Equations, and Software

1 code implementation21 Jan 2020 Adarsh Pyarelal, Marco A. Valenzuela-Escarcega, Rebecca Sharp, Paul D. Hein, Jon Stephens, Pratik Bhandari, HeuiChan Lim, Saumya Debray, Clayton T. Morrison

Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations.

Exotic Higgs Decays in Type-II 2HDMs at the LHC and Future 100 TeV Hadron Colliders

1 code implementation4 Dec 2018 Felix Kling, Honglei Li, Adarsh Pyarelal, Huayang Song, Shufang Su

The exotic decay modes of non-Standard Model (SM) Higgses in models with extended Higgs sectors have the potential to serve as powerful search channels to explore the space of Two-Higgs Doublet Models (2HDMs) that cannot be studied effectively using conventional decay channels.

High Energy Physics - Phenomenology

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