This talk considers the preference modeling problem and addresses the fact that pairwise comparison data often reflects irrational choice, e.g. intransitivity. Our key observation is that two items compared in isolation from other items may be compared based on only … Read More
S11 (2019-2020)
The Explanation Game: Towards Prediction Explainability through Sparse Communication
Explainability is a topic of growing importance in NLP. In this work, we provide a unified perspective of explainability as a communication problem between an explainer and a layperson about a classifier’s decision. We use this framework to compare several … Read More
Can 5G and Machine Learning Replace the GPS?
Whereas physical obstacles were mostly associated with signal attenuation in telecommunications, their presence in 5G’s millimeter wave systems adds complex, non-linear phenomena, including reflections and scattering. The result is a multipath propagation environment, shaped by the obstacles encountered during transmission, … Read More
Exploring Label Structure and Spatial Attention for Fashion Images Classification
In order to make decisions, for instance when purchasing a product, people rely on rich and accurate descriptions, which entail multi-label retrieval processes. However, multi-label classification is challenged by high dimensional and complex feature spaces and its dependency on large … Read More
Adaptively Sparse Transformers
Attention mechanisms have become ubiquitous in NLP. Recent architectures, notably the Transformer, learn powerful context-aware word representations through layered, multi-headed attention. The multiple heads learn diverse types of word relationships. However, with standard softmax attention, all attention heads are dense, … Read More
Evaluating Neural Methods for Approximate String Matching and Duplicate Detection
Duplicate detection concerns with identifying pairs of attributes/records that refer to the same real-world object, thus corresponding to a fundamental process when ensuring data quality in databases. Existing methods to detect duplicate attributes can leverage heuristic string similarity measures based … Read More
A Biologically Plausible Learning Algorithm for Artificial Neural Networks
Artificial neural networks, one of the most successful approaches to supervised learning, were originally inspired by their biological counterparts. However, the most successful learning algorithm for artificial neural networks, backpropagation, is considered biologically implausible. Many believe that the next generation … Read More
Variational Mixture of Normalizing Flows
In the past few years, deep generative models, such as generative adversarial networks, variational autoencoders, and their variants, have seen wide adoption for the task of modelling complex data distributions. In spite of the outstanding sample quality achieved by those … Read More
Sparse and Structured Visual Attention
Visual attention mechanisms are widely used in multimodal tasks, such as image captioning and visual question answering (VQA), being softmax attention mechanism the standard choice. One drawback of softmax-based attention mechanisms is that they assign probability mass to all image … Read More