Search Results for author: Tom M. Mitchell

Found 28 papers, 6 papers with code

SmartPlay: A Benchmark for LLMs as Intelligent Agents

1 code implementation2 Oct 2023 Yue Wu, Xuan Tang, Tom M. Mitchell, Yuanzhi Li

We introduce SmartPlay: both a challenging benchmark and a methodology for evaluating LLMs as agents.

The Roles of Symbols in Neural-based AI: They are Not What You Think!

no code implementations26 Apr 2023 Daniel L. Silver, Tom M. Mitchell

We propose that symbols are first and foremost external communication tools used between intelligent agents that allow knowledge to be transferred in a more efficient and effective manner than having to experience the world directly.

Inductive Bias

Transferable Student Performance Modeling for Intelligent Tutoring Systems

no code implementations8 Feb 2022 Robin Schmucker, Tom M. Mitchell

(ii) In the inductive transfer setting, we tune pre-trained course-agnostic performance models to new courses using small-scale target course data (e. g., collected during a pilot study).

Transfer Learning

Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy

1 code implementation4 Sep 2021 Robin Schmucker, Jingbo Wang, Shijia Hu, Tom M. Mitchell

This student performance (SP) modeling problem is a critical step for building adaptive online teaching systems.

Knowledge Tracing regression

Coarse-to-Fine Curriculum Learning

no code implementations8 Jun 2021 Otilia Stretcu, Emmanouil Antonios Platanios, Tom M. Mitchell, Barnabás Póczos

However, in machine learning, models are most often trained to solve the target tasks directly. Inspired by human learning, we propose a novel curriculum learning approach which decomposes challenging tasks into sequences of easier intermediate goals that are used to pre-train a model before tackling the target task.


Towards General Natural Language Understanding with Probabilistic Worldbuilding

2 code implementations6 May 2021 Abulhair Saparov, Tom M. Mitchell

We derive and implement an inference algorithm that reads sentences by parsing and abducing updates to its latent world model that capture the semantics of those sentences, and evaluate it on two out-of-domain question-answering datasets: (1) ProofWriter and (2) a new dataset we call FictionalGeoQA, designed to be more representative of real language but still simple enough to focus on evaluating reasoning ability, while being robust against heuristics.

Natural Language Understanding Question Answering +1

Fringe News Networks: Dynamics of US News Viewership following the 2020 Presidential Election

no code implementations22 Jan 2021 Ashiqur R. KhudaBukhsh, Rupak Sarkar, Mark S. Kamlet, Tom M. Mitchell

The growing political polarization of the American electorate over the last several decades has been widely studied and documented.

Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction

1 code implementation NeurIPS 2020 Mariya Toneva, Otilia Stretcu, Barnabas Poczos, Leila Wehbe, Tom M. Mitchell

These results suggest that only the end of semantic processing of a word is task-dependent, and pose a challenge for future research to formulate new hypotheses for earlier task effects as a function of the task and stimuli.

Discovering Bilingual Lexicons in Polyglot Word Embeddings

no code implementations31 Aug 2020 Ashiqur R. KhudaBukhsh, Shriphani Palakodety, Tom M. Mitchell

In this work, we utilize a single Skip-gram model trained on a multilingual corpus yielding polyglot word embeddings, and present a novel finding that a surprisingly simple constrained nearest-neighbor sampling technique in this embedding space can retrieve bilingual lexicons, even in harsh social media data sets predominantly written in English and Romanized Hindi and often exhibiting code switching.

Machine Translation Translation +1

Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks

no code implementations CL 2019 Mrinmaya Sachan, Avinava Dubey, Eduard H. Hovy, Tom M. Mitchell, Dan Roth, Eric P. Xing

At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information.

Interactive Task and Concept Learning from Natural Language Instructions and GUI Demonstrations

no code implementations30 Aug 2019 Toby Jia-Jun Li, Marissa Radensky, Justin Jia, Kirielle Singarajah, Tom M. Mitchell, Brad A. Myers

In this paper, we describe a new multi-modal domain-independent approach that combines natural language programming and programming-by-demonstration to allow users to first naturally describe tasks and associated conditions at a high level, and then collaborate with the agent to recursively resolve any ambiguities or vagueness through conversations and demonstrations.

Human-Computer Interaction

Competence-based Curriculum Learning for Neural Machine Translation

1 code implementation NAACL 2019 Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, Tom M. Mitchell

In this paper, we propose a curriculum learning framework for NMT that reduces training time, reduces the need for specialized heuristics or large batch sizes, and results in overall better performance.

Machine Translation NMT +1

Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems

no code implementations NeurIPS 2018 Mrinmaya Sachan, Kumar Avinava Dubey, Tom M. Mitchell, Dan Roth, Eric P. Xing

Finally, we also show how Nuts&Bolts can be used to achieve improvements on a relation extraction task and on the end task of answering Newtonian physics problems.

BIG-bench Machine Learning Relation Extraction

Discourse in Multimedia: A Case Study in Information Extraction

no code implementations13 Nov 2018 Mrinmaya Sachan, Kumar Avinava Dubey, Eduard H. Hovy, Tom M. Mitchell, Dan Roth, Eric P. Xing

At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information.

Merging knowledge bases in different languages

no code implementations WS 2017 Jer{\'o}nimo Hern{\'a}ndez-Gonz{\'a}lez, Estevam R. Hruschka Jr., Tom M. Mitchell

Recently, different systems which learn to populate and extend a knowledge base (KB) from the web in different languages have been presented.

Cultural Vocal Bursts Intensity Prediction

Machine Reading with Background Knowledge

no code implementations16 Dec 2016 Ndapandula Nakashole, Tom M. Mitchell

In this paper, we pursue a different approach; machine reading methods that make use of background knowledge to facilitate language understanding.

Prepositional Phrase Attachment Reading Comprehension

Learning a Compositional Semantics for Freebase with an Open Predicate Vocabulary

no code implementations TACL 2015 Jayant Krishnamurthy, Tom M. Mitchell

Crucially, our approach uses an open predicate vocabulary, enabling it to produce denotations for phrases such as {``}Republican front-runner from Texas{''} whose semantics cannot be represented using the Freebase schema.

Coreference Resolution Open Information Extraction +3

Zero-shot Learning with Semantic Output Codes

no code implementations NeurIPS 2009 Mark Palatucci, Dean Pomerleau, Geoffrey E. Hinton, Tom M. Mitchell

To achieve this, we define the notion of a semantic output code classifier (SOC) which utilizes a knowledge base of semantic properties of $Y$ to extrapolate to novel classes.

Zero-Shot Learning

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