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Found 22 papers, 8 papers with code
Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models
A restaurant dinner may become a memorable experience due to an unexpected aspect enjoyed by the customer, such as an origami-making station in the waiting area.
Can Language Models Employ the Socratic Method? Experiments with Code Debugging
When employing the Socratic method of teaching, instructors guide students toward solving a problem on their own rather than providing the solution directly.
Topic-Level Bayesian Surprise and Serendipity for Recommender Systems
One approach to mitigate this undesired behavior is to recommend items with high potential for serendipity, namely surprising items that are likely to be highly rated.
Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices
Many cloud applications are migrated from the monolithic model to a microservices framework in which hundreds of loosely-coupled microservices run concurrently, with significant benefits in terms of scalability, rapid development, modularity, and isolation.
LSTMs and Deep Residual Networks for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Management
In this work, we invert the "what-if" scenario and introduce a similar architecture based on chaining two LSTMs that can be trained to make either insulin or carbohydrate recommendations aimed at reaching a desired BG level in the future.
Changing the Narrative Perspective: From Deictic to Anaphoric Point of View
We introduce the task of changing the narrative point of view, where characters are assigned a narrative perspective that is different from the one originally used by the writer.
Mining Functionally Related Genes with Semi-Supervised Learning
In this paper, we introduce a rich set of features and use them in conjunction with semisupervised learning approaches in order to expand an initial set of seed genes to a larger cluster of functionally related genes.
From Note-Level to Chord-Level Neural Network Models for Voice Separation in Symbolic Music
We address this continuum by defining voice separation as the task of decomposing music into streams that exhibit both a high degree of external perceptual separation from the other streams and a high degree of internal perceptual consistency.
Distributed representation of patients and its use for medical cost prediction
Efficient representation of patients is very important in the healthcare domain and can help with many tasks such as medical risk prediction.
Figure Captioning with Reasoning and Sequence-Level Training
In this work, we investigate the problem of figure captioning where the goal is to automatically generate a natural language description of the figure.
Context Dependent Semantic Parsing over Temporally Structured Data
We describe a new semantic parsing setting that allows users to query the system using both natural language questions and actions within a graphical user interface.
Context-Dependent Semantic Parsing over Temporally Structured Data
We describe a new semantic parsing setting that allows users to query the system using both natural language questions and actions within a graphical user interface.
Chord Recognition in Symbolic Music: A Segmental CRF Model, Segment-Level Features, and Comparative Evaluations on Classical and Popular Music
We present a new approach to harmonic analysis that is trained to segment music into a sequence of chord spans tagged with chord labels.
Galaxy morphology prediction using capsule networks
In this work, we studied the performance of Capsule Network, a recently introduced neural network architecture that is rotationally invariant and spatially aware, on the task of galaxy morphology classification.
Training Ensembles to Detect Adversarial Examples
We propose a new ensemble method for detecting and classifying adversarial examples generated by state-of-the-art attacks, including DeepFool and C&W.
An Exploration of Data Augmentation and RNN Architectures for Question Ranking in Community Question Answering
The automation of tasks in community question answering (cQA) is dominated by machine learning approaches, whose performance is often limited by the number of training examples.
