Machine learning publications
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Model-Free Generative Replay For Lifelong Reinforcement Learning: Application To Starcraft-2
We evaluate our proposed algorithms on three different scenarios comprising tasks from the Starcraft 2 and Minigrid domains.
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Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models
Error maps can indicate when a correctly attended region may be processed incorrectly leading to an incorrect answer, and hence, improve users’ understanding of those cases.
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Challenges in Procedural Multimodal Machine Comprehension: A Novel Way to Benchmark
We identify three critical biases stemming from the question-answer generation process and memorization capabilities of large deep models.
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“How to best say it?” : Translating Directives in Machine Language into Natural Language in the Blocks World
We propose a method to generate optimal natural language for block placement directives generated by a machine’s planner during human-agent interactions in the blocks world.
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Comprehension Based Question Answering Using Bloom’s Taxonomy
Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions.
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Modular Adaptation for Cross-Domain Few-Shot Learning
While literature has demonstrated great successes via representation learning, in this work, we show that improvement of downstream tasks can also be achieved by appropriate designs of the adaptation process.
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Confidence Calibration for Domain Generalization under Covariate Shift
We present novel calibration solutions via domain generalization. Our core idea is to leverage multiple calibration domains to reduce the effective distribution disparity between the target and calibration domains for…
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Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning
We introduce Hybrid Consistency Training to jointly leverage interpolation consistency, including interpolating hidden features, that imposes linear behavior locally and data augmentation consistency that learns robust embeddings against sample variations.
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Lifelong learning using Eigentasks: Task separation, skill acquisition, and selective transfer
We introduce the eigentask framework for lifelong learning. An eigentask is a pairing of a skill that solves a set of related tasks, paired with a generative model that can…
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Toward Runtime Throttleable Neural Networks
This paper presents an approach to creating runtime-throttleable NNs that can adaptively balance performance and resource use in response to a control signal.
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Lucid Explanations Help: Using a Human-AI Image-Guessing Game to Evaluate Machine Explanation Helpfulness
We propose a Twenty-Questions style collaborative image retrieval game as a method of evaluating the efficacy of explanations (visual evidence or textual justification) in the context of Visual Question Answering.
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Spectral Convolutional Networks on Hierarchical Multigraphs
In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph…