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Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data. However, the primary focus has been on handling simple undirected graphs. Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated wi... | A Composition-based Graph Convolutional framework for multi-relational graphs. |
State-of-the-art neural machine translation methods employ massive amounts of parameters. Drastically reducing computational costs of such methods without affecting performance has been up to this point unsolved. In this work, we propose a quantization strategy tailored to the Transformer architecture. We evaluate our ... | We fully quantize the Transformer to 8-bit and improve translation quality compared to the full precision model. |
Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these ... | Latent Embedding Optimization (LEO) is a novel gradient-based meta-learner with state-of-the-art performance on the challenging 5-way 1-shot and 5-shot miniImageNet and tieredImageNet classification tasks. |
We introduce an approach for augmenting model-free deep reinforcement learning agents with a mechanism for relational reasoning over structured representations, which improves performance, learning efficiency, generalization, and interpretability. Our architecture encodes an image as a set of vectors, and applies an it... | Relational inductive biases improve out-of-distribution generalization capacities in model-free reinforcement learning agents |
Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain. It has been applied to numerous applications, such as data augmentation, domain adaptation, and unsupervised training. When paired training data is not a... | We propose a simple generative model for unsupervised image translation and saliency detection. |
Building deep neural networks to control autonomous agents which have to interact in real-time with the physical world, such as robots or automotive vehicles, requires a seamless integration of time into a network’s architecture. The central question of this work is, how the temporal nature of reality should be reflect... | We define a concept of layerwise model-parallel deep neural networks, for which layers operate in parallel, and provide a toolbox to design, train, evaluate, and on-line interact with these networks. |
Deep neural networks are known to be vulnerable to adversarial perturbations. In this paper, we bridge adversarial robustness of neural nets with Lyapunov stability of dynamical systems. From this viewpoint, training neural nets is equivalent to finding an optimal control of the discrete dynamical system, which allows ... | An adversarial defense method bridging robustness of deep neural nets with Lyapunov stability |
In this paper, we propose a method named Dimensional reweighting Graph Convolutional Networks (DrGCNs), to tackle the problem of variance between dimensional information in the node representations of GCNs. We prove that DrGCNs can reduce the variance of the node representations by connecting our problem to the theory ... | We propose a simple yet effective reweighting scheme for GCNs, theoretically supported by the mean field theory. |
Knowledge-grounded dialogue is a task of generating an informative response based on both discourse context and external knowledge. As we focus on better modeling the knowledge selection in the multi-turn knowledge-grounded dialogue, we propose a sequential latent variable model as the first approach to this matter. Th... | Our approach is the first attempt to leverage a sequential latent variable model for knowledge selection in the multi-turn knowledge-grounded dialogue. It achieves the new state-of-the-art performance on Wizard of Wikipedia benchmark. |
Meta-learning, or learning-to-learn, has proven to be a successful strategy in attacking problems in supervised learning and reinforcement learning that involve small amounts of data. State-of-the-art solutions involve learning an initialization and/or learning algorithm using a set of training episodes so that the met... | We propose a meta-learning method which efficiently amortizes hierarchical variational inference across training episodes. |
Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator. Knowledge distillation, the standard appr... | Representation/knowledge distillation by maximizing mutual information between teacher and student |
Developing effective biologically plausible learning rules for deep neural networks is important for advancing connections between deep learning and neuroscience. To date, local synaptic learning rules like those employed by the brain have failed to match the performance of backpropagation in deep networks. In this wor... | Networks that learn with feedback connections and local plasticity rules can be optimized for using meta learning. |
In the visual system, neurons respond to a patch of the input known as their classical receptive field (RF), and can be modulated by stimuli in the surround. These interactions are often mediated by lateral connections, giving rise to extra-classical RFs. We use supervised learning via backpropagation to learn feedforw... | CNNs with biologically-inspired lateral connections learned in an unsupervised manner are more robust to noisy inputs. |
Deep learning (DL) has in recent years been widely used in natural
language processing (NLP) applications due to its superior
performance. However, while natural languages are rich in
grammatical structure, DL has not been able to explicitly
represent and enforce such structures. This paper proposes a new
architec... | This paper is intended to develop a tensor product representation approach for deep-learning-based natural language processinig applications. |
It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived. However, existing certified robustnesses are limited to top-1 predictions. In many real-world applications, top-$k$ predictions are more re... | We study the certified robustness for top-k predictions via randomized smoothing under Gaussian noise and derive a tight robustness bound in L_2 norm. |
Recent work has shown increased interest in using the Variational Autoencoder (VAE) framework to discover interpretable representations of data in an unsupervised way. These methods have focussed largely on modifying the variational cost function to achieve this goal. However, we show that methods like beta-VAE simplif... | We present structured priors for unsupervised learning of disentangled representations in VAEs that significantly mitigate the trade-off between disentanglement and reconstruction loss. |
Due to the success of residual networks (resnets) and related architectures, shortcut connections have quickly become standard tools for building convolutional neural networks. The explanations in the literature for the apparent effectiveness of shortcuts are varied and often contradictory. We hypothesize that shortcut... | We generalize residual blocks to tandem blocks, which use arbitrary linear maps instead of shortcuts, and improve performance over ResNets. |
Adam-typed optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning. Such methods are appealing for capability on large-scale sparse datasets. On top of that, they are computationally efficient and insensit... | We present a new framework for adapting Adam-typed methods, namely AdamT, to include the trend information when updating the parameters with the adaptive step size and gradients. |
As machine learning methods see greater adoption and implementation in high stakes applications such
as medical image diagnosis, the need for model interpretability and explanation has become more
critical. Classical approaches that assess feature importance (eg saliency maps) do not explain how and why a particular re... | A method to explain a classifier, by generating visual perturbation of an image by exaggerating or diminishing the semantic features that the classifier associates with a target label. |
We study the problem of explaining a rich class of behavioral properties of deep neural networks. Our influence-directed explanations approach this problem by peering inside the network to identify neurons with high influence on the property of interest using an axiomatically justified influence measure, and then provi... | We present an influence-directed approach to constructing explanations for the behavior of deep convolutional networks, and show how it can be used to answer a broad set of questions that could not be addressed by prior work. |
Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily. By contrast, humans have an incredible ability to do one-shot or few-shot learning. For instance, from just hearing a word used in a sentence, humans can infer a great deal about it, by... | We highlight a technique by which natural language processing systems can learn a new word from context, allowing them to be much more flexible. |
Recent research developing neural network architectures with external memory have often used the benchmark bAbI question and answering dataset which provides a challenging number of tasks requiring reasoning. Here we employed a classic associative inference task from the human neuroscience literature in order to more c... | A memory architecture that support inferential reasoning. |
Depthwise separable convolutions reduce the number of parameters and computation used in convolutional operations while increasing representational efficiency.
They have been shown to be successful in image classification models, both in obtaining better models than previously possible for a given parameter count (the... | Depthwise separable convolutions improve neural machine translation: the more separable the better. |
Interpreting generative adversarial network (GAN) training as approximate divergence minimization has been
theoretically insightful, has spurred discussion, and has lead to theoretically and practically interesting
extensions such as f-GANs and Wasserstein GANs. For both classic GANs and f-GANs, there is an original ... | Non-saturating GAN training effectively minimizes a reverse KL-like f-divergence. |
We introduce a novel method for converting text data into abstract image representations, which allows image-based processing techniques (e.g. image classification networks) to be applied to text-based comparison problems. We apply the technique to entity disambiguation of inventor names in US patents. The method invol... | We introduce a novel text representation method which enables image classifiers to be applied to text classification problems, and apply the method to inventor name disambiguation. |
We propose a novel algorithm, Difference-Seeking Generative Adversarial Network (DSGAN), developed from traditional GAN. DSGAN considers the scenario that the training samples of target distribution, $p_{t}$, are difficult to collect.
Suppose there are two distributions $p_{\bar{d}}$ and $p_{d}$ such that the densit... | We proposed "Difference-Seeking Generative Adversarial Network" (DSGAN) model to learn the target distribution which is hard to collect training data. |
Recently, Generative Adversarial Network (GAN) and numbers of its variants have been widely used to solve the image-to-image translation problem and achieved extraordinary results in both a supervised and unsupervised manner. However, most GAN-based methods suffer from the imbalance problem between the generator and di... | A general method that improves the image translation performance of GAN framework by using an attention embedded discriminator |
The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are mainly restricted to dealing with unstructured evidence (e.g., natur... | We propose a new dataset to investigate the entailment problem under semi-structured table as premise |
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph neural network to obtain an initial ranking of soft correspondences between nodes. Secondly, we employ synchronous message passing networks ... | We develop a deep graph matching architecture which refines initial correspondences in order to reach neighborhood consensus. |
This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures.
By doing so the work parallels a more then a decade old results on mean-map embedding of probability measures in reproducing ker... | This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures. |
Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models. We propose Mahé, a novel approach to provide Model-Agnostic Hierarchical Explanations of how powerful machine learning models, such as deep ne... | A new framework for context-dependent and context-free explanations of predictions |
To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency. To this end, low-precision networks offer tremendous promise because both energy and area scale down quadratically with the reduction in precision. Here, for the first time, we demonst... | Finetuning after quantization matches or exceeds full-precision state-of-the-art networks at both 8- and 4-bit quantization. |
Analysis methods which enable us to better understand the
representations and functioning of neural models of language are
increasingly needed as deep learning becomes the dominant approach
in NLP. Here we present two methods based on Representational
Similarity Analysis (RSA) and Tree Kernels (TK) which allo... | Two methods based on Representational Similarity Analysis (RSA) and Tree Kernels (TK) which directly quantify how strongly information encoded in neural activation patterns corresponds to information represented by symbolic structures. |
Supervised deep learning requires a large amount of training samples with annotations (e.g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain. During the training of a deep neural network, the annotated samples are fed into the... | This paper introduces a framework for data-efficient representation learning by adaptive sampling in latent space. |
Existing methods for AI-generated artworks still struggle with generating high-quality stylized content, where high-level semantics are preserved, or separating fine-grained styles from various artists. We propose a novel Generative Adversarial Disentanglement Network which can disentangle two complementary factors of ... | An adversarial training-based method for disentangling two complementary sets of variations in a dataset where only one of them is labelled, tested on style vs. content in anime illustrations. |
Recent research has shown that CNNs are often overly sensitive to high-frequency textural patterns. Inspired by the intuition that humans are more sensitive to the lower-frequency (larger-scale) patterns we design a regularization scheme that penalizes large differences between adjacent components within each convoluti... | We introduce a smoothness regularization for convolutional kernels of CNN that can help improve adversarial robustness and lead to perceptually-aligned gradients |
Despite an ever growing literature on reinforcement learning algorithms and applications, much less is known about their statistical inference. In this paper, we investigate the large-sample behaviors of the Q-value estimates with closed-form characterizations of the asymptotic variances. This allows us to efficiently ... | We investigate the large-sample behaviors of the Q-value estimates and proposed an efficient exploration strategy that relies on estimating the relative discrepancies among the Q estimates. |
Entailment vectors are a principled way to encode in a vector what information is known and what is unknown. They are designed to model relations where one vector should include all the information in another vector, called entailment. This paper investigates the unsupervised learning of entailment vectors for the ... | We train word embeddings based on entailment instead of similarity, successfully predicting lexical entailment. |
We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against one another. Alice proposes a task for Bob to complete; and then Bob attempts to complete the task. In this work we will focus on two kinds ... | Unsupervised learning for reinforcement learning using an automatic curriculum of self-play |
Many real-world data sets are represented as graphs, such as citation links, social media, and biological interaction. The volatile graph structure makes it non-trivial to employ convolutional neural networks (CNN's) for graph data processing. Recently, graph attention network (GAT) has proven a promising attempt by co... | Exploiting rich strucural details in graph-structued data via adaptive "strucutral fingerprints'' |
Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people. We formulate this as a Bayesian Reinforcement Learning problem over latent Markov Decision Processes (MDPs). While Bayes-optimality is theoretically the gold standard, existing algorithms ... | We propose a scalable Bayesian Reinforcement Learning algorithm that learns a Bayesian correction over an ensemble of clairvoyant experts to solve problems with complex latent rewards and dynamics. |
One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural n... | We prove gradient descent achieves zero training loss with a linear rate on over-parameterized neural networks. |
For many applications, in particular in natural science, the task is to
determine hidden system parameters from a set of measurements. Often,
the forward process from parameter- to measurement-space is well-defined,
whereas the inverse problem is ambiguous: multiple parameter sets can
result in the same measurement... | To analyze inverse problems with Invertible Neural Networks |
Decisions made by machine learning systems have increasing influence on the world. Yet it is common for machine learning algorithms to assume that no such influence exists. An example is the use of the i.i.d. assumption in online learning for applications such as content recommendation, where the (choice of) content di... | Performance metrics are incomplete specifications; the ends don't always justify the means. |
In one-class-learning tasks, only the normal case can be modeled with data, whereas the variation of all possible anomalies is too large to be described sufficiently by samples. Thus, due to the lack of representative data, the wide-spread discriminative approaches cannot cover such learning tasks, and rather generativ... | We propose an anomaly-detection approach that combines modeling the foreground class via multiple local densities with adversarial training. |
Generative Adversarial Networks (GAN) can achieve promising performance on learning complex data distributions on different types of data. In this paper, we first show that a straightforward extension of an existing GAN algorithm is not applicable to point clouds, because the constraint required for discriminators is u... | We propose a GAN variant which learns to generate point clouds. Different studies have been explores, including tighter Wasserstein distance estimate, conditional generation, generalization to unseen point clouds and image to point cloud. |
Existing attention mechanisms, are mostly item-based in that a model is trained to attend to individual items in a collection (the memory) where each item has a predefined, fixed granularity, e.g., a character or a word. Intuitively, an area in the memory consisting of multiple items can be worth attending to as a whol... | The paper presents a novel approach for attentional mechanisms that can benefit a range of tasks such as machine translation and image captioning. |
We identify a phenomenon, which we refer to as *multi-model forgetting*, that occurs when sequentially training multiple deep networks with partially-shared parameters; the performance of previously-trained models degrades as one optimizes a subsequent one, due to the overwriting of shared parameters. To overcome this,... | We identify a phenomenon, neural brainwashing, and introduce a statistically-justified weight plasticity loss to overcome this. |
Revealing latent structure in data is an active field of research, having introduced exciting technologies such as variational autoencoders and adversarial networks, and is essential to push machine learning towards unsupervised knowledge discovery. However, a major challenge is the lack of suitable benchmarks for an o... | This paper introduces Morpho-MNIST, a collection of shape metrics and perturbations, in a step towards quantitative evaluation of representation learning. |
Exploration in environments with sparse rewards is a key challenge for reinforcement learning. How do we design agents with generic inductive biases so that they can explore in a consistent manner instead of just using local exploration schemes like epsilon-greedy? We propose an unsupervised reinforcement learning agen... | structured exploration in deep reinforcement learning via unsupervised visual abstraction discovery and control |
Combinatorial optimization is a common theme in computer science. While in general such problems are NP-Hard, from a practical point of view, locally optimal solutions can be useful. In some combinatorial problems however, it can be hard to define meaningful solution neighborhoods that connect large portions of the sea... | A new policy gradient algorithm designed to approach black-box combinatorial optimization problems. The algorithm relies only on function evaluations, and returns locally optimal solutions with high probability. |
Deterministic neural networks (NNs) are increasingly being deployed in safety critical domains, where calibrated, robust and efficient measures of uncertainty are crucial. While it is possible to train regression networks to output the parameters of a probability distribution by maximizing a Gaussian likelihood functio... | Fast, calibrated uncertainty estimation for neural networks without sampling |
The Lottery Ticket Hypothesis from Frankle & Carbin (2019) conjectures that, for typically-sized neural networks, it is possible to find small sub-networks which train faster and yield superior performance than their original counterparts. The proposed algorithm to search for such sub-networks (winning tickets), Iterat... | We propose a new algorithm that quickly finds winning tickets in neural networks. |
In most practical settings and theoretical analyses, one assumes that a model can be trained until convergence. However, the growing complexity of machine learning datasets and models may violate such assumptions. Indeed, current approaches for hyper-parameter tuning and neural architecture search tend to be limited by... | Introduce a formal setting for budgeted training and propose a budget-aware linear learning rate schedule |
We present a new approach for efficient exploration which leverages a low-dimensional encoding of the environment learned with a combination of model-based and model-free objectives. Our approach uses intrinsic rewards that are based on a weighted distance of nearest neighbors in the low dimensional representational sp... | We conduct exploration using intrinsic rewards that are based on a weighted distance of nearest neighbors in representational space. |
Neural networks are vulnerable to small adversarial perturbations. While existing literature largely focused on the vulnerability of learned models, we demonstrate an intriguing phenomenon that adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution. Even a semantics-preserving transf... | 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. |
Sample inefficiency is a long-lasting problem in reinforcement learning (RL). The state-of-the-art uses action value function to derive policy while it usually involves an extensive search over the state-action space and unstable optimization. Towards the sample-efficient RL, we propose ranking policy gradient (RPG),... | We propose ranking policy gradient that learns the optimal rank of actions to maximize return. We propose a general off-policy learning framework with the properties of optimality preserving, variance reduction, and sample-efficiency. |
We introduce MultiGrain, a neural network architecture that generates compact image embedding vectors that solve multiple tasks of different granularity: class, instance, and copy recognition. MultiGrain is trained jointly for classification by optimizing the cross-entropy loss and for instance/copy recognition by opti... | Combining classification and image retrieval in a neural network architecture, we obtain an improvement for both tasks. |
In this paper, we investigate mapping the hyponymy relation of
wordnet to feature vectors.
We aim to model lexical knowledge in such a way that it can be used as
input in generic machine-learning models, such as phrase entailment
predictors.
We propose two models. The first one leverages an existing mapping... | We investigate mapping the hyponymy relation of wordnet to feature vectors |
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models for learning prevalent patterns in natural language. Yet language generated by RNNs often shows several degenerate characteristics that are uncommon in human language; while fluent, RNN language production can be overly generic, repetitive,... | We build a stronger natural language generator by discriminatively training scoring functions that rank candidate generations with respect to various qualities of good writing. |
In recent years, the efficiency and even the feasibility of traditional load-balancing policies are challenged by the rapid growth of cloud infrastructure with increasing levels of server heterogeneity and increasing size of cloud services and applications. In such many software-load-balancers heterogeneous systems, tr... | Scalable and low communication load balancing solution for heterogeneous-server multi-dispatcher systems with strong theoretical guarantees and promising empirical results. |
We propose a novel quantitative measure to predict the performance of a deep neural network classifier, where the measure is derived exclusively from the graph structure of the network. We expect that this measure is a fundamental first step in developing a method to evaluate new network architectures and reduce the re... | A quantitative measure to predict the performances of deep neural network models. |
There is a stark disparity between the learning rate schedules used in the practice of large scale machine learning and what are considered admissible learning rate schedules prescribed in the theory of stochastic approximation. Recent results, such as in the 'super-convergence' methods which use oscillating learning r... | This paper presents a rigorous study of why practically used learning rate schedules (for a given computational budget) offer significant advantages even though these schemes are not advocated by the classical theory of Stochastic Approximation. |
We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments. We sh... | We present planners based on convnets that are sample-efficient and that generalize to larger instances of navigation and pathfinding problems. |
Learning high-quality word embeddings is of significant importance in achieving better performance in many down-stream learning tasks. On one hand, traditional word embeddings are trained on a large scale corpus for general-purpose tasks, which are often sub-optimal for many domain-specific tasks. On the other hand, ma... | learning better domain embeddings via lifelong learning and meta-learning |
Parameter pruning is a promising approach for CNN compression and acceleration by eliminating redundant model parameters with tolerable performance loss. Despite its effectiveness, existing regularization-based parameter pruning methods usually drive weights towards zero with large and constant regularization factors, ... | we propose a new regularization-based pruning method (named IncReg) to incrementally assign different regularization factors to different weight groups based on their relative importance. |
Momentum based stochastic gradient methods such as heavy ball (HB) and Nesterov's accelerated gradient descent (NAG) method are widely used in practice for training deep networks and other supervised learning models, as they often provide significant improvements over stochastic gradient descent (SGD). Rigorously speak... | Existing momentum/acceleration schemes such as heavy ball method and Nesterov's acceleration employed with stochastic gradients do not improve over vanilla stochastic gradient descent, especially when employed with small batch sizes. |
Oversubscription planning (OSP) is the problem of finding plans that maximize the utility value of their end state while staying within a specified cost bound. Recently, it has been shown that OSP problems can be reformulated as classical planning problems with multiple cost functions but no utilities. Here we take a... | We show that oversubscription planning tasks can be solved using A* and introduce novel bound-sensitive heuristics for oversubscription planning tasks. |
Previous work on adversarially robust neural networks requires large training sets and computationally expensive training procedures. On the other hand, few-shot learning methods are highly vulnerable to adversarial examples. The goal of our work is to produce networks which both perform well at few-shot tasks and ... | We develop meta-learning methods for adversarially robust few-shot learning. |
Many of our core assumptions about how neural networks operate remain empirically untested. One common assumption is that convolutional neural networks need to be stable to small translations and deformations to solve image recognition tasks. For many years, this stability was baked into CNN architectures by incorporat... | We find that pooling alone does not determine deformation stability in CNNs and that filter smoothness plays an important role in determining stability. |
Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time. To overcome this problem, we present a simple and effective method self-ensemble label filtering (SELF) to progressively filter out the wrong labels during training. Our method improves the tas... | We propose a self-ensemble framework to train more robust deep learning models under noisy labeled datasets. |
Long training times of deep neural networks are a bottleneck in machine learning research. The major impediment to fast training is the quadratic growth of both memory and compute requirements of dense and convolutional layers with respect to their information bandwidth. Recently, training `a priori' sparse networks ha... | We investigate pruning DNNs before training and provide an answer to which topology should be used for training a priori sparse networks. |
Deep learning models require extensive architecture design exploration and hyperparameter optimization to perform well on a given task. The exploration of the model design space is often made by a human expert, and optimized using a combination of grid search and search heuristics over a large space of possible choices... | We present Multitask Neural Model Search, a Meta-learner that can design models for multiple tasks simultaneously and transfer learning to unseen tasks. |
This work studies the problem of modeling non-linear visual processes by leveraging deep generative architectures for learning linear, Gaussian models of observed sequences. We propose a joint learning framework, combining a multivariate autoregressive model and deep convolutional generative networks. After justificati... | We model non-linear visual processes as autoregressive noise via generative deep learning. |
Partial differential equations (PDEs) play a prominent role in many disciplines such as applied mathematics, physics, chemistry, material science, computer science, etc. PDEs are commonly derived based on physical laws or empirical observations. However, the governing equations for many complex systems in modern appli... | This paper proposes a new feed-forward network, call PDE-Net, to learn PDEs from data. |
Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism. However, such simple approximate posteriors are often ins... | We give a fast normalising-flow like sampling procedure for discrete latent variable models. |
Deep neural networks (DNNs) had great success on NLP tasks such as language modeling, machine translation and certain question answering (QA) tasks. However, the success is limited at more knowledge intensive tasks such as QA from a big corpus. Existing end-to-end deep QA models (Miller et al., 2016; Weston et al., 201... | We propose a framework that learns to encode knowledge symbolically and generate programs to reason about the encoded knowledge. |
We propose to use a meta-learning objective that maximizes the speed of transfer on a modified distribution to learn how to modularize acquired knowledge. In particular, we focus on how to factor a joint distribution into appropriate conditionals, consistent with the causal directions. We explain when this can work, us... | This paper proposes a meta-learning objective based on speed of adaptation to transfer distributions to discover a modular decomposition and causal variables. |
Continual learning is a longstanding goal of artificial intelligence, but is often counfounded by catastrophic forgetting that prevents neural networks from learning tasks sequentially. Previous methods in continual learning have demonstrated how to mitigate catastrophic forgetting, and learn new tasks while retaining ... | Another perspective on catastrophic forgetting |
We propose an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attribute
editing workflow. Current face modeling methods using 3DMM suffer from the lack of local control. We thus create a 3DMM by
combining local part-based 3DMM for the eyes, nose, mouth, ears, and faci... | We propose an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attribute editing workflow by selecting the best sets of eigenvectors and anthropometric measurements. |
We review eight machine learning classification algorithms to analyze Electroencephalographic (EEG) signals in order to distinguish EEG patterns associated with five basic educational tasks. There is a large variety of classifiers being used in this EEG-based Brain-Computer Interface (BCI) field. While previous EEG exp... | Two Algorithms outperformed eight others on a EEG-based BCI experiment |
Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems. In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction. We introduce two communication protoc... | We teach agents to negotiate using only reinforcement learning; selfish agents can do so, but only using a trustworthy communication channel, and prosocial agents can negotiate using cheap talk. |
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class. The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and general... | We propose a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem. |
We describe the use of an automated scheduling system for observation policy design and to schedule operations of the NASA (National Aeronautics and Space Administration) ECOSystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). We describe the adaptation of the Compressed Large-scale Activity Sc... | We describe the use of an automated scheduling system for observation policy design and to schedule operations of NASA's ECOSTRESS mission. |
Adversarial examples are modified samples that preserve original image structures but deviate classifiers. Researchers have put efforts into developing methods for generating adversarial examples and finding out origins. Past research put much attention on decision boundary changes caused by these methods. This paper, ... | Hybird storage and representation of learned knowledge may be a reason for adversarial examples. |
Differently from the popular Deep Q-Network (DQN) learning, Alternating Q-learning (AltQ) does not fully fit a target Q-function at each iteration, and is generally known to be unstable and inefficient. Limited applications of AltQ mostly rely on substantially altering the algorithm architecture in order to improve its... | New Experiments and Theory for Adam Based Q-Learning |
In search for more accurate predictive models, we customize capsule networks for the learning to diagnose problem. We also propose Spectral Capsule Networks, a novel variation of capsule networks, that converge faster than capsule network with EM routing. Spectral capsule networks consist of spatial coincidence filter... | A new capsule network that converges faster on our healthcare benchmark experiments. |
One of the big challenges in machine learning applications is that training data can be different from the real-world data faced by the algorithm. In language modeling, users’ language (e.g. in private messaging) could change in a year and be completely different from what we observe in publicly available data. At the ... | We propose a method of distributed fine-tuning of language models on user devices without collection of private data |
We propose that approximate Bayesian algorithms should optimize a new criterion, directly derived from the loss, to calculate their approximate posterior which we refer to as pseudo-posterior. Unlike standard variational inference which optimizes a lower bound on the log marginal likelihood, the new algorithms can be a... | This paper utilizes the analysis of Lipschitz loss on a bounded hypothesis space to derive new ERM-type algorithms with strong performance guarantees that can be applied to the non-conjugate sparse GP model. |
In this paper, we propose a novel regularization method, RotationOut, for neural networks.
Different from Dropout that handles each neuron/channel independently, RotationOut regards its input layer as an entire vector and introduces regularization by randomly rotating the vector.
RotationOut can also be used in con... | We propose a regularization method for neural network and a noise analysis method |
Formulating the reinforcement learning (RL) problem in the framework of probabilistic inference not only offers a new perspective about RL, but also yields practical algorithms that are more robust and easier to train. While this connection between RL and probabilistic inference has been extensively studied in the sing... | A probabilistic framework for multi-agent reinforcement learning |
Sorting input objects is an important step in many machine learning pipelines. However, the sorting operator is non-differentiable with respect to its inputs, which prohibits end-to-end gradient-based optimization. In this work, we propose NeuralSort, a general-purpose continuous relaxation of the output of the sorting... | We provide a continuous relaxation to the sorting operator, enabling end-to-end, gradient-based stochastic optimization. |
Transferring knowledge across tasks to improve data-efficiency is one of
the open key challenges in the area of global optimization algorithms. Readily
available algorithms are typically designed to be universal optimizers and, thus,
often suboptimal for specific tasks. We propose a novel transfer learning method to... | We perform efficient and flexible transfer learning in the framework of Bayesian optimization through meta-learned neural acquisition functions. |
We study the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory. The theory suggests that DNN training comprises a rapid fitting phase followed by a slower compression phase, in which the mutual information I(X... | Deterministic deep neural networks do not discard information, but they do cluster their inputs. |
A central challenge in multi-agent reinforcement learning is the induction of coordination between agents of a team. In this work, we investigate how to promote inter-agent coordination using policy regularization and discuss two possible avenues respectively based on inter-agent modelling and synchronized sub-policy s... | We propose regularization objectives for multi-agent RL algorithms that foster coordination on cooperative tasks. |
Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work l... | We present a model that learns robust joint representations by performing hierarchical cyclic translations between multiple modalities. |
The geometric properties of loss surfaces, such as the local flatness of a solution, are associated with generalization in deep learning. The Hessian is often used to understand these geometric properties. We investigate the differences between the eigenvalues of the neural network Hessian evaluated over the empirical ... | Understanding the neural network Hessian eigenvalues under the data generating distribution. |
Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph compone... | One simple trick to improve sequence models: Compose them with a graph model |
In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability. Nevertheless, it is difficult to determine the number of components. We propose a sparse classifier based on a discriminative Gaussian mixture model (GMM), which is named sparse discriminative Gaussia... | A sparse classifier based on a discriminative Gaussian mixture model, which can also be embedded into a neural network. |
We recently observed that convolutional filters initialized
farthest apart from each other using offthe-
shelf pre-computed Grassmannian subspace
packing codebooks performed surprisingly well
across many datasets. Through this short paper,
we’d like to disseminate some initial results in this
regard in the hope t... | Initialize weights using off-the-shelf Grassmannian codebooks, get faster training and better accuracy |
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