Search Results for author: Roland Fernandez

Found 8 papers, 5 papers with code

Enriching Transformers with Structured Tensor-Product Representations for Abstractive Summarization

1 code implementation NAACL 2021 Yichen Jiang, Asli Celikyilmaz, Paul Smolensky, Paul Soulos, Sudha Rao, Hamid Palangi, Roland Fernandez, Caitlin Smith, Mohit Bansal, Jianfeng Gao

On several syntactic and semantic probing tasks, we demonstrate the emergent structural information in the role vectors and improved syntactic interpretability in the TPR layer outputs.

Abstractive Text Summarization

Compositional Processing Emerges in Neural Networks Solving Math Problems

1 code implementation19 May 2021 Jacob Russin, Roland Fernandez, Hamid Palangi, Eric Rosen, Nebojsa Jojic, Paul Smolensky, Jianfeng Gao

A longstanding question in cognitive science concerns the learning mechanisms underlying compositionality in human cognition.

Mathematical Reasoning

A System for Real-Time Interactive Analysis of Deep Learning Training

1 code implementation5 Jan 2020 Shital Shah, Roland Fernandez, Steven Drucker

To achieve this, we model various exploratory inspection and diagnostic tasks for deep learning training processes as specifications for streams using a map-reduce paradigm with which many data scientists are already familiar.

3D Action Recognition

Working Memory Graphs

no code implementations ICML 2020 Ricky Loynd, Roland Fernandez, Asli Celikyilmaz, Adith Swaminathan, Matthew Hausknecht

Transformers have increasingly outperformed gated RNNs in obtaining new state-of-the-art results on supervised tasks involving text sequences.

Decision Making

Enhancing the Transformer with Explicit Relational Encoding for Math Problem Solving

2 code implementations15 Oct 2019 Imanol Schlag, Paul Smolensky, Roland Fernandez, Nebojsa Jojic, Jürgen Schmidhuber, Jianfeng Gao

We incorporate Tensor-Product Representations within the Transformer in order to better support the explicit representation of relation structure.

Question Answering

Learning and analyzing vector encoding of symbolic representations

no code implementations10 Mar 2018 Roland Fernandez, Asli Celikyilmaz, Rishabh Singh, Paul Smolensky

We present a formal language with expressions denoting general symbol structures and queries which access information in those structures.

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