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ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars
TLDR
This work explores an in-situ processing approach, where memristor crossbar arrays not only store input weights, but are also used to perform dot-product operations in an analog manner.
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DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning
TLDR
DeepLog, a deep neural network model utilizing Long Short-Term Memory (LSTM), is proposed, to model a system log as a natural language sequence, which allows DeepLog to automatically learn log patterns from normal execution, and detect anomalies when log patterns deviate from the model trained from log data under normal execution.
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Importance of Semantic Representation: Dataless Classification
TLDR
This paper introduces Dataless Classification, a learning protocol that uses world knowledge to induce classifiers without the need for any labeled data, and proposes a model for dataless classification and shows that the label name alone is often sufficient to induceclassifiers.
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Modeling Biological Processes for Reading Comprehension
TLDR
This paper focuses on a new reading comprehension task that requires complex reasoning over a single document, and demonstrates that answering questions via predicted structures substantially improves accuracy over baselines that use shallower representations.
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Discriminative Learning over Constrained Latent Representations
TLDR
A novel joint learning algorithm for both NLP decision problems, that uses the final prediction to guide the selection of the best intermediate representation, is developed.
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INFOTABS: Inference on Tables as Semi-structured Data
In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between…
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On Measuring and Mitigating Biased Inferences of Word Embeddings
TLDR
A mechanism for measuring stereotypes using the task of natural language inference is designed and a reduction in invalid inferences via bias mitigation strategies on static word embeddings (GloVe), and it is shown that for gender bias, these techniques extend to contextualizedembeddings when applied selectively only to the static components of contextualized embeddeds.
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Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
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An NLP Curator (or: How I Learned to Stop Worrying and Love NLP Pipelines)
TLDR
Curator, an NLP management framework designed to address some common problems and inefficiencies associated with building NLP process pipelines; and Edison, a NLP data structure library in Java that provides streamlined interactions with Curator and offers a range of useful supporting functionality.
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UNQOVERing Stereotypical Biases via Underspecified Questions
TLDR
UNQOVER, a general framework to probe and quantify biases through underspecified questions, is presented, showing that a naive use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors: positional dependence and question independence.
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