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Publications
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences
We propose a hierarchical model for sequential data that learns a tree on-the-fly, i.e. while reading the sequence. In the model, a recurren… (voir plus)t network adapts its structure and reuses recurrent weights in a recursive manner. This creates adaptive skip-connections that ease the learning of long-term dependencies. The tree structure can either be inferred without supervision through reinforcement learning, or learned in a supervised manner. We provide preliminary experiments in a novel Math Expression Evaluation (MEE) task, which is created to have a hierarchical tree structure that can be used to study the effectiveness of our model. Additionally, we test our model in a well-known propositional logic and language modelling tasks. Experimental results have shown the potential of our approach.
We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as s… (voir plus)ummarization and dialogue systems. We introduce two new corpora, derived from the Penn Treebank and the Annotated English Gigaword dataset and investigate the use of a novel attention mechanism tailored to this task. Our attention mechanism augments a baseline recurrent neural network without the need for additional trainable parameters, minimizing the added computational cost of our mechanism. We demonstrate that this model statistically outperforms our baselines.
2018-07-01
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word seque… (voir plus)nces in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.
In this work, we propose a novel constituency parsing scheme. The model first predicts a real-valued scalar, named syntactic distance, for e… (voir plus)ach split position in the sentence. The topology of grammar tree is then determined by the values of syntactic distances. Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. It is also easier to parallelize and much faster. Our model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, which surpasses the previous single model results by a large margin.
2018-07-01
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (publié)
The backpropagation of error algorithm (BP) is impossible to implement in a real brain. The recent success of deep networks in machine learn… (voir plus)ing and AI, however, has inspired proposals for understanding how the brain might learn across multiple layers, and hence how it might approximate BP. As of yet, none of these proposals have been rigorously evaluated on tasks where BP-guided deep learning has proved critical, or in architectures more structured than simple fully-connected networks. Here we present results on scaling up biologically motivated models of deep learning on datasets which need deep networks with appropriate architectures to achieve good performance. We present results on the MNIST, CIFAR-10, and ImageNet datasets and explore variants of target-propagation (TP) and feedback alignment (FA) algorithms, and explore performance in both fully- and locally-connected architectures. We also introduce weight-transport-free variants of difference target propagation (DTP) modified to remove backpropagation from the penultimate layer. Many of these algorithms perform well for MNIST, but for CIFAR and ImageNet we find that TP and FA variants perform significantly worse than BP, especially for networks composed of locally connected units, opening questions about whether new architectures and algorithms are required to scale these approaches. Our results and implementation details help establish baselines for biologically motivated deep learning schemes going forward.
Reinforcement learning (RL) has recently achieved tremendous success in solving complex tasks. Careful considerations are made towards repro… (voir plus)ducible research in machine learning. Reproducibility in RL often becomes more difficult, due to the lack of standard evaluation method and detailed methodology for algorithms and comparisons with existing work. In this work, we highlight key differences in evaluation in RL compared to supervised learning, and discuss specific issues that are often non-intuitive for newcomers. We study the importance of reproducibility in evaluation in RL, and propose an evaluation pipeline that can be decoupled from the algorithm code. We hope such an evaluation pipeline can be standardized, as a step towards robust and reproducible research in RL.
It is well known that over-parametrized deep neural networks (DNNs) are an overly expressive class of functions that can memorize even rando… (voir plus)m data with
Motivation
The computational prediction of a protein structure from its sequence generally relies on a method to assess the quality of prote… (voir plus)in models. Most assessment methods rank candidate models using heavily engineered structural features, defined as complex functions of the atomic coordinates. However, very few methods have attempted to learn these features directly from the data.
Results
We show that deep convolutional networks can be used to predict the ranking of model structures solely on the basis of their raw three-dimensional atomic densities, without any feature tuning. We develop a deep neural network that performs on par with state-of-the-art algorithms from the literature. The network is trained on decoys from the CASP7 to CASP10 datasets and its performance is tested on the CASP11 dataset. Additional testing on decoys from the CASP12, CAMEO and 3DRobot datasets confirms that the network performs consistently well across a variety of protein structures. While the network learns to assess structural decoys globally and does not rely on any predefined features, it can be analyzed to show that it implicitly identifies regions that deviate from the native structure.
Availability and implementation
The code and the datasets are available at https://github.com/lamoureux-lab/3DCNN_MQA.
Supplementary information
Supplementary data are available at Bioinformatics online.
We focus on two supervised visual reasoning tasks whose labels encode a semantic relational rule between two or more objects in an image: th… (voir plus)e MNIST Parity task and the colorized Pentomino task. The objects in the images undergo random translation, scaling, rotation and coloring transformations. Thus these tasks involve invariant relational reasoning. We report uneven performance of various deep CNN models on these two tasks. For the MNIST Parity task, we report that the VGG19 model soundly outperforms a family of ResNet models. Moreover, the family of ResNet models exhibits a general sensitivity to random initialization for the MNIST Parity task. For the colorized Pentomino task, now both the VGG19 and ResNet models exhibit sluggish optimization and very poor test generalization, hovering around 30% test error. The CNN we tested all learn hierarchies of fully distributed features and thus encode the distributed representation prior. We are motivated by a hypothesis from cognitive neuroscience which posits that the human visual cortex is modularized, and this allows the visual cortex to learn higher order invariances. To this end, we consider a modularized variant of the ResNet model, referred to as a Residual Mixture Network (ResMixNet) which employs a mixture-of-experts architecture to interleave distributed representations with more specialized, modular representations. We show that very shallow ResMixNets are capable of learning each of the two tasks well, attaining less than 2% and 1% test error on the MNIST Parity and the colorized Pentomino tasks respectively. Most importantly, the ResMixNet models are extremely parameter efficient: generalizing better than various non-modular CNNs that have over 10x the number of parameters. These experimental results support the hypothesis that modularity is a robust prior for learning invariant relational reasoning.
State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed … (voir plus)of poolings/strided convolutions or successive dilated convolutions. However, it is not clear which operation leads to best results. In this paper, we systematically study the differences introduced by distinct receptive field enlargement methods and their impact on the performance of a novel architecture, called Fully Convolutional DenseResNet (FC-DRN). FC-DRN has a densely connected backbone composed of residual networks. Following standard image segmentation architectures, receptive field enlargement operations that change the representation level are interleaved among residual networks. This allows the model to exploit the benefits of both residual and dense connectivity patterns, namely: gradient flow, iterative refinement of representations, multi-scale feature combination and deep supervision. In order to highlight the potential of our model, we test it on the challenging CamVid urban scene understanding benchmark and make the following observations: 1) downsampling operations outperform dilations when the model is trained from scratch, 2) dilations are useful during the finetuning step of the model, 3) coarser representations require less refinement steps, and 4) ResNets (by model construction) are good regularizers, since they can reduce the model capacity when needed. Finally, we compare our architecture to alternative methods and report state-of-the-art result on the Camvid dataset, with at least twice fewer parameters.
2018-06-18
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publié)
We study the challenges of applying deep learning to gene expression data. We find experimentally that there exists non-linear signal in the… (voir plus) data, however is it not discovered automatically given the noise and low numbers of samples used in most research. We discuss how gene interaction graphs (same pathway, protein-protein, co-expression, or research paper text association) can be used to impose a bias on a deep model similar to the spatial bias imposed by convolutions on an image. We explore the usage of Graph Convolutional Neural Networks coupled with dropout and gene embeddings to utilize the graph information. We find this approach provides an advantage for particular tasks in a low data regime but is very dependent on the quality of the graph used. We conclude that more work should be done in this direction. We design experiments that show why existing methods fail to capture signal that is present in the data when features are added which clearly isolates the problem that needs to be addressed.
Deep networks often perform well on the data manifold on which they are trained, yet give incorrect (and often very confident) answers when … (voir plus)evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and domain shift. We propose Manifold Mixup which encourages the network to produce more reasonable and less confident predictions at points with combinations of attributes not seen in the training set. This is accomplished by training on convex combinations of the hidden state representations of data samples. Using this method, we demonstrate improved semi-supervised learning, learning with limited labeled data, and robustness to adversarial examples. Manifold Mixup requires no (significant) additional computation. Analytical experiments on both real data and synthetic data directly support our hypothesis for why the Manifold Mixup method improves results.
