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Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversar...	An unsupervised domain adaptation approach which adapts at both the pixel and feature levels
Stemming is the process of removing affixes( i.e. prefixes, infixes and suffixes) that improve the accuracy and performance of information retrieval systems.This paper presents the reduction of Amharic words to corresponding stem where with the intention that it preserves semantic information. The proposed approach eff...	Amharic Light Stemmer is designed for improving performance of Amharic Sentiment Classification.
Place and grid-cells are known to aid navigation in animals and humans. Together with concept cells, they allow humans to form an internal representation of the external world, namely the concept space. We investigate the presence of such a space in deep neural networks by plotting the activation profile of its hidden ...	We investigated if simple deep networks possess grid cell-like artificial neurons while memory retrieval in the learned concept space.
We develop a comprehensive description of the active inference framework, as proposed by Friston (2010), under a machine-learning compliant perspective. Stemming from a biological inspiration and the auto-encoding principles, a sketch of a cognitive architecture is proposed that should provide ways to implement estimat...	Pros and cons of saccade-based computer vision under a predictive coding perspective
Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare. To circumvent this problem and learn on graphs, graph feature representation is required. Main difficulties with feature extraction lie in the trade-off between expressiveness,...	We study theoretically the consistency the Laplacian spectrum and use it as whole-graph embeddding
Adversarial training, a method for learning robust deep networks, is typically assumed to be more expensive than traditional training due to the necessity of constructing adversarial examples via a first-order method like projected gradient decent (PGD). In this paper, we make the surprising discovery that it is poss...	FGSM-based adversarial training, with randomization, works just as well as PGD-based adversarial training: we can use this to train a robust classifier in 6 minutes on CIFAR10, and 12 hours on ImageNet, on a single machine.
In seeking for sparse and efficient neural network models, many previous works investigated on enforcing L1 or L0 regularizers to encourage weight sparsity during training. The L0 regularizer measures the parameter sparsity directly and is invariant to the scaling of parameter values. But it cannot provide useful gradi...	We propose almost everywhere differentiable and scale invariant regularizers for DNN pruning, which can lead to supremum sparsity through standard SGD training.
Self-supervision, in which a target task is improved without external supervision, has primarily been explored in settings that assume the availability of additional data. However, in many cases, particularly in healthcare, one may not have access to additional data (labeled or otherwise). In such settings, we hypothes...	We show that extra unlabeled data is not required for self-supervised auxiliary tasks to be useful for time series classification, and present new and effective auxiliary tasks.
Are neural networks biased toward simple functions? Does depth always help learn more complex features? Is training the last layer of a network as good as training all layers? These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective. We will...	Eigenvalues of Conjugate (aka NNGP) and Neural Tangent Kernel can be computed in closed form over the Boolean cube and reveal the effects of hyperparameters on neural network inductive bias, training, and generalization.
To communicate, to ground hypotheses, to analyse data, neuroscientists often refer to divisions of the brain. Here we consider atlases used to parcellate the brain when studying brain function. We discuss the meaning and the validity of these parcellations, from a conceptual point of view as well as by running various ...	All functional brain parcellations are wrong, but some are useful
High-dimensional sparse reward tasks present major challenges for reinforcement learning agents. In this work we use imitation learning to address two of these challenges: how to learn a useful representation of the world e.g. from pixels, and how to explore efficiently given the rarity of a reward signal? We show ...	Imitation from pixels, with sparse or no reward, using off-policy RL and a tiny adversarially-learned reward function.
In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework. One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them. The other strategy learns formal specifications from the real data and show...	We show strategies to easily identify fake samples generated with the Generative Adversarial Network framework.
Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to an...	We present an analytical framework to determine accumulation bit-width requirements in all three deep learning training GEMMs and verify the validity and tightness of our method via benchmarking experiments.
Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain. However, the two predominant methods, domain discrepancy reduction learning and semi-supervised learning, are not readily applicable when source and target ...	A new theory of unsupervised domain adaptation for distance metric learning and its application to face recognition across diverse ethnicity variations.
In this paper, we consider the problem of training neural networks (NN). To promote a NN with specific structures, we explicitly take into consideration the nonsmooth regularization (such as L1-norm) and constraints (such as interval constraint). This is formulated as a constrained nonsmooth nonconvex optimization prob...	We propose a convergent proximal-type stochastic gradient descent algorithm for constrained nonsmooth nonconvex optimization problems
The loss of a few neurons in a brain rarely results in any visible loss of function. However, the insight into what “few” means in this context is unclear. How many random neuron failures will it take to lead to a visible loss of function? In this paper, we address the fundamental question of the impact of the crash of...	We give a bound for NNs on the output error in case of random weight failures using a Taylor expansion in the continuous limit where nearby neurons are similar
Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world. In robotics, we have seen tremendous progress in using visual and tactile perception; however we have often ignored a key sense: sound. This is primarily due to lack of data that captures th...	We explore and study the synergies between sound and action.
Hierarchical label structures widely exist in many machine learning tasks, ranging from those with explicit label hierarchies such as image classification to the ones that have latent label hierarchies such as semantic segmentation. Unfortunately, state-of-the-art methods often utilize cross-entropy loss which in-expli...	We propose Hierarchical Complement Objective Training, a novel training paradigm to effectively leverage category hierarchy in the labeling space on both image classification and semantic segmentation.
There is a growing interest in automated neural architecture search (NAS). To improve the efficiency of NAS, previous approaches adopt weight sharing method to force all models share the same set of weights. However, it has been observed that a model performing better with shared weights does not necessarily perform...	Our paper identifies the issue of existing weight sharing approach in neural architecture search and propose a practical method, achieving strong results.
Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels. In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized. Then, we re...	We propose a novel two-phase training approach based on "early stopping" for robust training on noisy labels.
Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Co...	We introduce IC3Net, a single network which can be used to train agents in cooperative, competitive and mixed scenarios. We also show that agents can learn when to communicate using our model.
Neural sequence-to-sequence models are a recently proposed family of approaches used in abstractive summarization of text documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text. Despite the advances in abstractive summarization, c...	We present the first neural abstractive summarization model capable of customization of generated summaries.
We propose a software framework based on ideas of the Learning-Compression algorithm , that allows one to compress any neural network by different compression mechanisms (pruning, quantization, low-rank, etc.). By design, the learning of the neural net (handled by SGD) is decoupled from the compression of its parameter...	We propose a software framework based on ideas of the Learning-Compression algorithm , that allows one to compress any neural network by different compression mechanisms (pruning, quantization, low-rank, etc.).
This work seeks the possibility of generating the human face from voice solely based on the audio-visual data without any human-labeled annotations. To this end, we propose a multi-modal learning framework that links the inference stage and generation stage. First, the inference networks are trained to match the speake...	This paper proposes a method of end-to-end multi-modal generation of human face from speech based on a self-supervised learning framework.
We present a simple neural model that given a formula and a property tries to answer the question whether the formula has the given property, for example whether a propositional formula is always true. The structure of the formula is captured by a feedforward neural network recursively built for the given formula in a ...	A top-down approach how to recursively represent propositional formulae by neural networks is presented.
Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games. We identify three key challenges that any algorithm needs to master in order to perform well on all games: proces...	Ape-X DQfD = Distributed (many actors + one learner + prioritized replay) DQN with demonstrations optimizing the unclipped 0.999-discounted return on Atari.
The knowledge that humans hold about a problem often extends far beyond a set of training data and output labels. While the success of deep learning mostly relies on supervised training, important properties cannot be inferred efficiently from end-to-end annotations alone, for example causal relations or domain-specifi...	Training method to enforce strict constraints on learned embeddings during supervised training. Applied to visual question answering.
Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems. On the other hand, for many problems, classic algorithms exist, which typically exceed the accuracy and stability of neural networks. To combine these two ...	Solving inverse problems by using smooth approximations of the forward algorithms to train the inverse models.
Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain. In this work, we focus on weakly supervised localization (WSL) where a model is trained to classify an image and localize reg...	A deep learning method for weakly-supervised pointwise localization that learns using image-level label only. It relies on conditional entropy to localize relevant and irrelevant regions aiming to minimize false positive regions.
Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. Particularly, Dyna architecture, as an elegant model-based architecture integrating learning and planning, provides huge flexibility of using a model. One of the most important components in Dyna ...	Acquire states from high frequency region for search-control in Dyna.
We propose a new architecture for distributed image compression from a group of distributed data sources. The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy. The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scala...	We introduce a data-driven Distributed Source Coding framework based on Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC).
Long short-term memory networks (LSTMs) were introduced to combat vanishing gradients in simple recurrent neural networks (S-RNNs) by augmenting them with additive recurrent connections controlled by gates. We present an alternate view to explain the success of LSTMs: the gates themselves are powerful recurrent models ...	Gates do all the heavy lifting in LSTMs by computing element-wise weighted sums, and removing the internal simple RNN does not degrade model performance.
Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine ...	By analyzing more than 300 papers in recent machine learning conferences, we found that Machine Learning for Health (ML4H) applications lag behind other machine learning fields in terms of reproducibility metrics.
We propose a solution for evaluation of mathematical expression. However, instead of designing a single end-to-end model we propose a Lego bricks style architecture. In this architecture instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one spec...	We train many small networks each for a specific operation, these are then combined to perform complex operations
In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real. The generator is trained to increase the probability that fake data is real. We argue that it should also simultaneously decrease the probability that real data is real because 1) this would accou...	Improving the quality and stability of GANs using a relativistic discriminator; IPM GANs (such as WGAN-GP) are a special case.
Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting. However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entro...	A state-value function-based version of MPO that achieves good results in a wide range of tasks in discrete and continuous control.
Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence. There has been a significant body of work studying neural networks that mimic general computation, but these networks fail to generalize to data distributions that are outside of their training set. We study ...	We propose neural execution engines (NEEs), which leverage a learned mask and supervised execution traces to mimic the functionality of subroutines and demonstrate strong generalization.
Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks. However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task,...	Bayesian changepoint detection enables meta-learning directly from time series data.
People with high-frequency hearing loss rely on hearing aids that employ frequency lowering algorithms. These algorithms shift some of the sounds from the high frequency band to the lower frequency band where the sounds become more perceptible for the people with the condition. Fricative phonemes have an important part...	A deep learning based approach for zero delay fricative phoneme detection
Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction. To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptiv...	An online and linear-time attention mechanism that performs soft attention over adaptively-located chunks of the input sequence.
We present a framework for automatically ordering image patches that enables in-depth analysis of dataset relationship to learnability of a classification task using convolutional neural network. An image patch is a group of pixels residing in a continuous area contained in the sample. Our preliminary experimental resu...	Develop new techniques that rely on patch reordering to enable detailed analysis of data-set relationship to training and generalization performances.
Producing agents that can generalize to a wide range of environments is a significant challenge in reinforcement learning. One method for overcoming this issue is domain randomization, whereby at the start of each training episode some parameters of the environment are randomized so that the agent is exposed to many po...	We produce reinforcement learning agents that generalize well to a wide range of environments using a novel regularization technique.
Claims from the fields of network neuroscience and connectomics suggest that topological models of the brain involving complex networks are of particular use and interest. The field of deep neural networks has mostly left inspiration from these claims out. In this paper, we propose three architectures and use each of t...	We explore the intersection of network neurosciences and deep learning.
Creating a knowledge base that is accurate, up-to-date and complete remains a significant challenge despite substantial efforts in automated knowledge base construction. In this paper, we present Alexandria -- a system for unsupervised, high-precision knowledge base construction. Alexandria uses a probabilistic progr...	This paper presents a system for unsupervised, high-precision knowledge base construction using a probabilistic program to define a process of converting knowledge base facts into unstructured text.
Recent advances have made it possible to create deep complex-valued neural networks. Despite this progress, many challenging learning tasks have yet to leverage the power of complex representations. Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequ...	New Signal Extraction Method in the Fourier Domain
We propose an implementation of GNN that predicts and imitates the motion be- haviors from observed swarm trajectory data. The network’s ability to capture interaction dynamics in swarms is demonstrated through transfer learning. We finally discuss the inherent availability and challenges in the scalability of GNN, and...	Improve the scalability of graph neural networks on imitation learning and prediction of swarm motion
Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings. Despite their effectiveness, the number of parameters in an embedding layer increases linearly with the number of symbols and poses a critical challenge on memory and storage constraints. I...	We propose a differentiable product quantization framework that can reduce the size of embedding layer in an end-to-end training at no performance cost.
For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean. Modal regression approaches aim to instead find the conditional mode, but are restricted to nonparametric...	We introduce a simple and novel modal regression algorithm which is easy to scale to large problems.
Deep reinforcement learning algorithms require large amounts of experience to learn an individual task. While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality. Current methods rely heavi...	Sample efficient meta-RL by combining variational inference of probabilistic task variables with off-policy RL
Knowledge bases, massive collections of facts (RDF triples) on diverse topics, support vital modern applications. However, existing knowledge bases contain very little data compared to the wealth of information on the Web. This is because the industry standard in knowledge base creation and augmentation suffers from a ...	This paper focuses on identifying high quality web sources for industrial knowledge base augmentation pipeline.
We explore the match prediction problem where one seeks to estimate the likelihood of a group of M items preferred over another, based on partial group comparison data. Challenges arise in practice. As existing state-of-the-art algorithms are tailored to certain statistical models, we have different best algorithms acr...	We investigate the merits of employing neural networks in the match prediction problem where one seeks to estimate the likelihood of a group of M items preferred over another, based on partial group comparison data.
Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients. Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs). We propose a ...	A novel approach to maintain orthogonal recurrent weight matrices in a RNN.
A large number of natural language processing tasks exist to analyze syntax, semantics, and information content of human language. These seemingly very different tasks are usually solved by specially designed architectures. In this paper, we provide the simple insight that a great variety of tasks can be represented in...	We use a single model to solve a great variety of natural language analysis tasks by formulating them in a unified span-relation format.
Large matrix inversions have often been cited as a major impediment to scaling Gaussian process (GP) models. With the use of GPs as building blocks for ever more sophisticated Bayesian deep learning models, removing these impediments is a necessary step for achieving large scale results. We present a variational approx...	We present a variational lower bound for GP models that can be optimised without computing expensive matrix operations like inverses, while providing the same guarantees as existing variational approximations.
It has been shown that using geometric spaces with non-zero curvature instead of plain Euclidean spaces with zero curvature improves performance on a range of Machine Learning tasks for learning representations. Recent work has leveraged these geometries to learn latent variable models like Variational Autoencoders (VA...	Variational Autoencoders with latent spaces modeled as products of constant curvature Riemannian manifolds improve on image reconstruction over single-manifold variants.
Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery. We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties. Remarkably, we observe that when generated mole...	We introduce a black box algorithm for repeated optimization of compounds using a translation framework.
Deep Neural Networks (DNNs) are increasingly deployed in cloud servers and autonomous agents due to their superior performance. The deployed DNN is either leveraged in a white-box setting (model internals are publicly known) or a black-box setting (only model outputs are known) depending on the application. A practical...	Proposing the first watermarking framework for multi-bit signature embedding and extraction using the outputs of the DNN.
Adversarial training provides a principled approach for training robust neural networks. From an optimization perspective, the adversarial training is essentially solving a minmax robust optimization problem. The outer minimization is trying to learn a robust classifier, while the inner maximization is trying to genera...	Don't know how to optimize? Then just learn to optimize!
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty. It is based on a simple idea of disentangling com- ponents of the future state which are predictable from those which are inherently unpredictable, and encoding the unpredictable components into a low-dimens...	A simple and easy to train method for multimodal prediction in time series.
Conducting reinforcement-learning experiments can be a complex and timely process. A full experimental pipeline will typically consist of a simulation of an environment, an implementation of one or many learning algorithms, a variety of additional components designed to facilitate the agent-environment interplay, and a...	This paper introduces and motivates simple_rl, a new open source library for carrying out reinforcement learning experiments in Python 2 and 3 with a focus on simplicity.
Wasserstein GAN(WGAN) is a model that minimizes the Wasserstein distance between a data distribution and sample distribution. Recent studies have proposed stabilizing the training process for the WGAN and implementing the Lipschitz constraint. In this study, we prove the local stability of optimizing the simple gradien...	This paper deals with stability of simple gradient penalty $\mu$-WGAN optimization by introducing a concept of measure valued differentiation.
We present Random Partition Relaxation (RPR), a method for strong quantization of the parameters of convolutional neural networks to binary (+1/-1) and ternary (+1/0/-1) values. Starting from a pretrained model, we first quantize the weights and then relax random partitions of them to their continuous values for retrai...	State-of-the-art training method for binary and ternary weight networks based on alternating optimization of randomly relaxed weight partitions
Learning long-term dependencies is a key long-standing challenge of recurrent neural networks (RNNs). Hierarchical recurrent neural networks (HRNNs) have been considered a promising approach as long-term dependencies are resolved through shortcuts up and down the hierarchy. Yet, the memory requirements of Truncated Bac...	We replace some gradients paths in hierarchical RNN's by an auxiliary loss. We show that this can reduce the memory cost while preserving performance.
In a typical deep learning approach to a computer vision task, Convolutional Neural Networks (CNNs) are used to extract features at varying levels of abstraction from an image and compress a high dimensional input into a lower dimensional decision space through a series of transformations. In this paper, we investigate...	Neural networks that do a good job of classification project points into more spherical shapes before compressing them into fewer dimensions.
Deep learning methods have achieved high performance in sound recognition tasks. Deciding how to feed the training data is important for further performance improvement. We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning). Our strategy is to learn a discriminative feature...	We propose an novel learning method for deep sound recognition named BC learning.
Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and so on. While numerous studies have been conducted, most existing works assume that the data from d...	We propose a method that infers the time-varying data quality level for spatiotemporal forecasting without explicitly assigned labels.
Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts. In contrast, recent advances in 3D shape sensing focus more on low-level...	We propose 3D shape programs, a structured, compositional shape representation. Our model learns to infer and execute shape programs to explain 3D shapes.
Deep Reinforcement Learning (Deep RL) has been receiving increasingly more attention thanks to its encouraging performance on a variety of control tasks. Yet, conventional regularization techniques in training neural networks (e.g., $L_2$ regularization, dropout) have been largely ignored in RL methods, possibly becau...	We show that conventional regularization methods (e.g., $L_2$, dropout), which have been largely ignored in RL methods, can be very effective in policy optimization.
We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. We formulate our reasoning task by generating ...	We present a question-answering dataset, FigureQA, as a first step towards developing models that can intuitively recognize patterns from visual representations of data.
In this paper, I discuss some varieties of explanation that can arise in intelligent agents. I distinguish between process accounts, which address the detailed decisions made during heuristic search, and preference accounts, which clarify the ordering of alternatives independent of how they were generated. I also h...	This position paper analyzes different types of self explanation that can arise in planning and related systems.
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic results, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views....	The first deep learning approach to MFSR to solve registration, fusion, up-sampling in an end-to-end manner.
Large mini-batch parallel SGD is commonly used for distributed training of deep networks. Approaches that use tightly-coupled exact distributed averaging based on AllReduce are sensitive to slow nodes and high-latency communication. In this work we show the applicability of Stochastic Gradient Push (SGP) for distribute...	For distributed training over high-latency networks, use gossip-based approximate distributed averaging instead of exact distribute averaging like AllReduce.
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on. The ...	This paper develops an adversarial learning framework for neural conversation models with persona
We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions. To simulate properties of biological systems we add the costs penalizing long connections and the proximity of neurons in a two-dimensional space. Our experiments show that in the case wh...	Bio-inspired artificial neural networks, consisting of neurons positioned in a two-dimensional space, are capable of forming independent groups for performing different tasks.
The transformer has become a central model for many NLP tasks from translation to language modeling to representation learning. Its success demonstrates the effectiveness of stacked attention as a replacement for recurrence for many tasks. In theory attention also offers more insights into the model’s internal decision...	Discrete transformer which uses hard attention to ensure that each step only depends on a fixed context.
Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex. W...	We show empirical evidence that predictive coding models yield representations more correlated to brain data than supervised image recognition models.
The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples. Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest. Ideally one would like to allow both the data f...	A generic framework for handling transfer and multi-task learning using pairs of autoencoders with task-specific and shared weights.
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the two via adaptive neur...	We propose a framework to combine decision trees and neural networks, and show on image classification tasks that it enjoys the complementary benefits of the two approaches, while addressing the limitations of prior work.
While natural language processing systems often focus on a single language, multilingual transfer learning has the potential to improve performance, especially for low-resource languages. We introduce XLDA, cross-lingual data augmentation, a method that replaces a segment of the input text with its translation in ano...	Translating portions of the input during training can improve cross-lingual performance.
Training conditional generative latent-variable models is challenging in scenarios where the conditioning signal is very strong and the decoder is expressive enough to generate a plausible output given only the condition; the generative model tends to ignore the latent variable, suffering from posterior collapse. We f...	We propose a conditional variational autoencoder framework that mitigates the posterior collapse in scenarios where the conditioning signal strong enough for an expressive decoder to generate a plausible output from it.
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed. We propose a methodology for testing for statistical differences in model performances under several replications. To study this specifi...	We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning. In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach. Accordingly, the choice of representation -- the mapping of ...	We translate a bound on sub-optimality of representations to a practical training objective in the context of hierarchical reinforcement learning.
Heuristic search research often deals with finding algorithms for offline planning which aim to minimize the number of expanded nodes or planning time. In online planning, algorithms for real-time search or deadline-aware search have been considered before. However, in this paper, we are interested in the problem of {\...	Metareasoning in a Situated Temporal Planner
Neural networks are vulnerable to small adversarial perturbations. Existing literature largely focused on understanding and mitigating the vulnerability of learned models. In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Advers...	Robustness performance of PGD trained models are sensitive to semantics-preserving transformation of image datasets, which implies the trickiness of evaluation of robust learning algorithms in practice.
Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity. Typically this comparison is done either at the word level or at the sentence level, with no attempt to leverage the inherent structure of the sentence. When sentence structure is u...	Matching sentences by learning the latent constituency tree structures with a variant of the inside-outside algorithm embedded as a neural network layer.
Learning disentangled representation from any unlabelled data is a non-trivial problem. In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learns powerful disentangled representation through maximizing the mutual information between the representation and given information in an unsu...	Learn disentangle representation in an unsupervised manner.
Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt networks generalise poorly. Data Augmentation (Krizhevsky et al., 2012) alleviates this by using existing data more effectively. However standard data augmentation produces only limited plaus...	Conditional GANs trained to generate data augmented samples of their conditional inputs used to enhance vanilla classification and one shot learning systems such as matching networks and pixel distance
Answering questions about data can require understanding what parts of an input X influence the response Y. Finding such an understanding can be built by testing relationships between variables through a machine learning model. For example, conditional randomization tests help determine whether a variable relates to th...	We develop a simple regression-based model-agnostic feature selection method to interpret data generating processes with FDR control, and outperform several popular baselines on several simulated, medical, and image datasets.
Supervised learning depends on annotated examples, which are taken to be the ground truth. But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk. Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the classic crowdsourcing probl...	A new approach for learning a model from noisy crowdsourced annotations.
Neural networks make mistakes. The reason why a mistake is made often remains a mystery. As such neural networks often are considered a black box. It would be useful to have a method that can give an explanation that is intuitive to a user as to why an image is misclassified. In this paper we develop a method for expla...	New way of explaining why a neural network has misclassified an image
In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand. Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers. Understandably, as the number ...	A method for the automated construction of branched multi-task networks with strong experimental evaluation on diverse multi-tasking datasets.
Typical recent neural network designs are primarily convolutional layers, but the tricks enabling structured efficient linear layers (SELLs) have not yet been adapted to the convolutional setting. We present a method to express the weight tensor in a convolutional layer using diagonal matrices, discrete cosine transfor...	It's possible to substitute the weight matrix in a convolutional layer to train it as a structured efficient layer; performing as well as low-rank decomposition.
Blind document deblurring is a fundamental task in the field of document processing and restoration, having wide enhancement applications in optical character recognition systems, forensics, etc. Since this problem is highly ill-posed, supervised and unsupervised learning methods are well suited for this application. U...	We present SVDocNet, an end-to-end trainable U-Net based spatial recurrent neural network (RNN) for blind document deblurring.
In contrast to the monolithic deep architectures used in deep learning today for computer vision, the visual cortex processes retinal images via two functionally distinct but interconnected networks: the ventral pathway for processing object-related information and the dorsal pathway for processing motion and transform...	We extend bilinear sparse coding and leverage video sequences to learn dynamic filters.
Conventional out-of-distribution (OOD) detection schemes based on variational autoencoder or Random Network Distillation (RND) are known to assign lower uncertainty to the OOD data than the target distribution. In this work, we discover that such conventional novelty detection schemes are also vulnerable to the blurre...	We propose a novel OOD detector that employ blurred images as adversarial examples . Our model achieve significant OOD detection performance in various domains.
Training large deep neural networks on massive datasets is computationally very challenging. There has been recent surge in interest in using large batch stochastic optimization methods to tackle this issue. The most prominent algorithm in this line of research is LARS, which by employing layerwise adaptive learning ...	A fast optimizer for general applications and large-batch training.
Model-agnostic meta-learning (MAML) is known as a powerful meta-learning method. However, MAML is notorious for being hard to train because of the existence of two learning rates. Therefore, in this paper, we derive the conditions that inner learning rate $\alpha$ and meta-learning rate $\beta$ must satisfy for MAML to...	We analyzed the role of two learning rates in model-agnostic meta-learning in convergence.
We present a neural framework for learning associations between interrelated groups of words such as the ones found in Subject-Verb-Object (SVO) structures. Our model induces a joint function-specific word vector space, where vectors of e.g. plausible SVO compositions lie close together. The model retains information a...	Task-independent neural model for learning associations between interrelated groups of words.
The fabrication of semiconductor involves etching process to remove selected areas from wafers. However, the measurement of etched structure in micro-graph heavily relies on time-consuming manual routines. Traditional image processing usually demands on large number of annotated data and the performance is still poor. ...	Using deep learning method to carry out automatic measurement of SEM images in semiconductor industry
Generating and scheduling activities is particularly challenging when considering both consumptive resources and complex resource interactions such as time-dependent resource usage.We present three methods of determining valid temporal placement intervals for an activity in a temporally grounded plan in the presen...	This paper describes and analyzes three methods to schedule non-fixed duration activities in the presence of consumptive resources.

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