14 Oct 2019
- Date: Friday October 18, 2019
- Time: 12:00PM
- Location: Rice 242
Title: Neural Message Passing for Multi-Label Classification
Speaker: Jack Lanchantin
Abstract: Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. We propose Label Message Passing (LaMP) Neural Networks to efficiently model the joint prediction of multiple labels. LaMP treats labels as nodes on a label-interaction graph and computes the hidden representation of each label node conditioned on the input using attention-based neural message passing. Attention enables LaMP to assign different importance to neighbor nodes per label, learning how labels interact (implicitly). The proposed models are simple, accurate, interpretable, structure-agnostic, and applicable for predicting dense labels since LaMP is incredibly parallelizable. We validate the benefits of LaMP on seven real-world MLC datasets, covering a broad spectrum of input/output types and outperforming the state-of-the-art results. Notably, LaMP enables intuitive interpretation of how classifying each label depends on the elements of a sample and at the same time rely on its interaction with other labels.
Title: FastGSK: Fast and Efficient SequenceAnalysis using Gapped String Kernels
Speaker: Derrick Blakely
Abstract: Character-level string kernel methods achieve strong classification performance on DNA, protein, and text data using modestly-sized training sets. However, existing methods suffer from slow kernel computation time and overfitting, owing to the exponential dependence between the alphabet size and n-gram length. In this work, we introduce a new character-level string kernel algorithm using gapped n-grams, called FastGSK. We formulate the kernel function as a series of independent counting operations, which we sample to obtain a fast approximation algorithm. This work enables state-of-the-art string kernel methods to scale to any alphabet size (DNA, protein, or natural language) and use longer features. Moreover, we use a modern software architecture with a multithreaded implementation backend and a PyPi package frontend. We experimentally show that FastGSK matches or outperforms existing string kernel methods, as well as recurrent neural networks, across a variety of sequence analysis tasks.
07 Oct 2019
- Date: Friday October 11, 2019
- Time: 12:00PM
- Location: Rice 242
Title: Optimizing regularization parameters of image processing algorithms through machine learning
Speaker: Tanjin Taher Toma
Abstract: In image and video processing problems (e.g., enhancement, reconstruction, segmentation, etc.), algorithms often have regularization parameters that need to be set appropriately to obtain good results. Existing automatic parameter selection techniques are mostly iterative and often relies on a predetermined image metric (such as, image quality, or risk estimate) to estimate parameter value. In this talk, we discuss our convolutional neural network approach for direct parameter estimation that demonstrates effectiveness over existing methods.
Title: Myocardial T1 mapping with convolutional neural networks
Speaker: Haris Jeelani
Abstract: The longitudinal relaxation time (T1) of the hydrogen protons in the heart wall can be used as an indicator for a variety of pathological conditions. Traditionally, a pixel-wise nonlinear model fitting sensitive to noise is used to obtain T1 maps. As discussed in my previous AIML talk, to increase the noise robustness we were using a convolutional neural network framework (DeepT1). In this talk I will discuss the updates we made to our DeepT1 framework. The updated model includes a recurrent and a U-net model to improve the performance of T1-map estimation. This is joint work work Dr. Michael Salerno and Dr. Christopher Kramer (Cardiology) and Dr. Yang Yang (now, Mount Sinai School of Medicine).
Title: Examining Working Memory Representations for Neural Networks Trained to Play Games
Speaker: Tyler Spears (supervised by Per Sederberg, Psychology)
Abstract: The current success of deep learning is owed, in no small part, to the field’s roots in cognitive neuroscience. In this work, we examine the properties of several human-based models of working memory (WM), and analyze their computational utility when combined with deep neural networks. We then put forth the Scale-Invariant Temporal History (SITH) model, an applied variant of a WM model recently proposed in the cognitive neuroscience literature. Finally, we discuss future applications of SITH in artificial intelligence, as well as the future of neurally-inspired machine learning methods. This work was supervised by Prof. Per Sederberg (Psychology).
30 Sep 2019
Fuwen Tan
Department of Computer Science
University of Virginia
- Date: Friday October 4th, 2019
- Time: 12:00PM
- Location: Rice 242
Abstract This work explores the task of interactive image retrieval using natural language queries, where a user progressively provides input queries to refine a set of retrieval results. Moreover, our work explores this problem in the context of complex image scenes containing multiple objects. We propose Drill-down, an effective framework for encoding multiple queries with an efficient compact state representation that significantly extends current methods for single-round image retrieval. We show that using multiple rounds of natural language queries as input can be surprisingly effective to find arbitrarily specific images of complex scenes. Furthermore, we find that existing image datasets with textual captions can provide a surprisingly effective form of weak supervision for this task. We compare our method with existing sequential encoding and embedding networks, demonstrating superior performance on two proposed benchmarks: automatic image retrieval on a simulated scenario that uses region captions as queries, and interactive image retrieval using real queries from human evaluators.
27 Sep 2019
Wanyu Du
Department of Computer Science
University of Virginia
- Date: Friday September 27th, 2019
- Time: 12:00PM
- Location: Rice 242
Abstract Paraphrase generation is a fundamental research problem that can benefit many other downstream NLP tasks, such as machine translation, text generation, document summarization, and question answering. Previous methods mainly use supervised learning to learn a paraphrase decoder. Given a source sentence, a decoder will decode the target words step by step from a large vocabulary. However, this type of learning and decoding method always suffers from the exposure bias: the current prediction is conditioned on the ground-truth during training but on previous predictions during decoding, and it may lead to propagating and accumulating errors when decoding the text. To deal with this challenge, both reinforcement learning (RL) and imitation learning (IL) have been widely studied, but the lack of direct comparison leads to only a partial image on their benefits. In this work, we present an empirical study on how RL and IL can help boost the performance of generating paraphrases, with the pointer-generator as a base model. And our experiments on the benchmark datasets show that: (1) imitation learning can constantly perform better than reinforcement learning, and (2) the pointer-generator model with imitation learning can outperform the state-of-the-art methods with a large margin.