10 citations · 39 across the 10 of their papers we have counts for
11 papers
Multi-Agent Dynamic Pricing in a Blockchain Protocol Using Gaussian Bandits
Alexis Asseman, Tomasz Kornuta, Anirudh Patel +2
The Graph Protocol indexes historical blockchain transaction data and makes it available for querying. As the protocol is decentralized, there are many independent Indexers that in…
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…
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…
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…
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…
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…
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…
Using Multi-task and Transfer Learning to Solve Working Memory Tasks
T. S. Jayram, Tomasz Kornuta, Ryan L. McAvoy +1
We propose a new architecture called Memory-Augmented Encoder-Solver (MAES) that enables transfer learning to solve complex working memory tasks adapted from cognitive psychology.…
Object-based reasoning in VQA
Mikyas T. Desta, Larry Chen, Tomasz Kornuta
Visual Question Answering (VQA) is a novel problem domain where multi-modal inputs must be processed in order to solve the task given in the form of a natural language. As the solu…
Utilization of Deep Reinforcement Learning for saccadic-based object visual search
Tomasz Kornuta, Kamil Rocki
The paper focuses on the problem of learning saccades enabling visual object search. The developed system combines reinforcement learning with a neural network for learning to pred…
Surprisal-Driven Zoneout
Kamil Rocki, Tomasz Kornuta, Tegan Maharaj
We propose a novel method of regularization for recurrent neural networks called suprisal-driven zoneout. In this method, states zoneout (maintain their previous value rather than…