activity
20182020
most citedA Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset

10 citations · 21 across the 4 of their papers we have counts for

collaborators

8 papers

cs.LG202010 cited

A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset

Vahid Noroozi, Yang Zhang, Evelina Bakhturina +1

Dialog State Tracking (DST) is one of the most crucial modules for goal-oriented dialogue systems. In this paper, we introduce FastSGT (Fast Schema Guided Tracker), a fast and robu…

cs.CV20191 cited

Transfer Learning in Visual and Relational Reasoning

T. S. Jayram, Vincent Marois, Tomasz Kornuta +3

Transfer learning has become the de facto standard in computer vision and natural language processing, especially where labeled data is scarce. Accuracy can be significantly improv…

cs.LG20191 cited

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Tomasz Kornuta

Access to vast amounts of data along with affordable computational power stimulated the reincarnation of neural networks. The progress could not be achieved without adequate softwa…

cs.CV20199 cited

Leveraging Medical Visual Question Answering with Supporting Facts

Tomasz Kornuta, Deepta Rajan, Chaitanya Shivade +2

In this working notes paper, we describe IBM Research AI (Almaden) team's participation in the ImageCLEF 2019 VQA-Med competition. The challenge consists of four question-answering…

cs.CV2018

On transfer learning using a MAC model variant

Vincent Marois, T. S. Jayram, Vincent Albouy +3

We introduce a variant of the MAC model (Hudson and Manning, ICLR 2018) with a simplified set of equations that achieves comparable accuracy, while training faster. We evaluate bot…

cs.LG2018

Learning to Remember, Forget and Ignore using Attention Control in Memory

T. S. Jayram, Younes Bouhadjar, Ryan L. McAvoy +4

Typical neural networks with external memory do not effectively separate capacity for episodic and working memory as is required for reasoning in humans. Applying knowledge gained…