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We propose a context-adaptive entropy model for use in end-to-end optimized image compression. Our model exploits two types of contexts, bit-consuming contexts and bit-free contexts, distinguished based upon whether additional bit
allocation is required. Based on these contexts, we allow the model to more accurately e... | Context-adaptive entropy model for use in end-to-end optimized image compression, which significantly improves compression performance |
Deep networks often perform well on the data distribution on which they are trained, yet give incorrect (and often very confident) answers when evaluated on points from off of the training distribution. This is exemplified by the adversarial examples phenomenon but can also be seen in terms of model generalization and ... | A method for learning better representations, that acts as a regularizer and despite its no significant additional computation cost , achieves improvements over strong baselines on Supervised and Semi-supervised Learning tasks. |
Sometimes SRS (Stereotactic Radio Surgery) requires using sphere packing on a Region of Interest (ROI) such as cancer to determine a treatment plan. We have developed a sphere packing algorithm which packs non-intersecting spheres inside the ROI. The region of interest in our case are those voxels which are identif... | Packing region of Interest (ROI) such as cancerous regions identified in 3D Volume Data, Packing spheres inside the ROI, rotating the ROI , measures of difference in sphere packing before and after the rotation. |
We show implicit filter level sparsity manifests in convolutional neural networks (CNNs) which employ Batch Normalization and ReLU activation, and are trained using adaptive gradient descent techniques with L2 regularization or weight decay. Through an extensive empirical study (Anonymous, 2019) we hypothesize the mech... | Filter level sparsity emerges implicitly in CNNs trained with adaptive gradient descent approaches due to various phenomena, and the extent of sparsity can be inadvertently affected by different seemingly unrelated hyperparameters. |
Humans can learn a variety of concepts and skills incrementally over the course of their lives while exhibiting an array of desirable properties, such as non-forgetting, concept rehearsal, forward transfer and backward transfer of knowledge, few-shot learning, and selective forgetting. Previous approaches to lifelong m... | Drawing parallels with human learning, we propose a unified framework to exhibit many lifelong learning abilities in neural networks by utilizing a small number of weight consolidation parameters. |
Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved. In this work we study how pre-trained generative adversarial networks (GANs) can be used to clean noisy, highly artifact laden reconstructions from conventional techniques, by ... | We show that robust GAN priors work better than GAN priors for limited angle CT reconstruction which is a highly under-determined inverse problem. |
We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision. We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a p... | A new form of attention that works well for the distant supervision setting, and a multitask learning approach to add sentence-level annotations. |
The attention layer in a neural network model provides insights into the model’s reasoning behind its prediction, which are usually criticized for being opaque. Recently, seemingly contradictory viewpoints have emerged about the interpretability of attention weights (Jain & Wallace, 2019; Vig & Belinkov, 2019). Amid su... | Analysis of attention mechanism across diverse NLP tasks. |
Generative models forsource code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output. Ou... | Representing programs as graphs including semantics helps when generating programs |
The inference of models, prediction of future symbols, and entropy rate estimation of discrete-time, discrete-event processes is well-worn ground. However, many time series are better conceptualized as continuous-time, discrete-event processes. Here, we provide new methods for inferring models, predicting future symbol... | A new method for inferring a model of, estimating the entropy rate of, and predicting continuous-time, discrete-event processes. |
Recent studies show that convolutional neural networks (CNNs) are vulnerable under various settings, including adversarial examples, backdoor attacks, and distribution shifting. Motivated by the findings that human visual system pays more attention to global structure (e.g., shape) for recognition while CNNs are bias... | A unified model to improve model robustness against multiple tasks |
Learning rich representation from data is an important task for deep generative models such as variational auto-encoder (VAE). However, by extracting high-level abstractions in the bottom-up inference process, the goal of preserving all factors of variations for top-down generation is compromised. Motivated by the conc... | We proposed a progressive learning method to improve learning and disentangling latent representations at different levels of abstraction. |
Deep latent variable models are powerful tools for representation learning. In this paper, we adopt the deep information bottleneck model, identify its shortcomings and propose a model that circumvents them. To this end, we apply a copula transformation which, by restoring the invariance properties of the information b... | We apply the copula transformation to the Deep Information Bottleneck which leads to restored invariance properties and a disentangled latent space with superior predictive capabilities. |
State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements. We introduce a novel method to systematically construct such benchmar... | Benchmark and method to measure compositional generalization by maximizing divergence of compound frequency at small divergence of atom frequency. |
To understand how object vision develops in infancy and childhood, it will be necessary to develop testable computational models. Deep neural networks (DNNs) have proven valuable as models of adult vision, but it is not yet clear if they have any value as models of development. As a first model, we measured learning in... | Unsupervised networks learn from bottom up; machines and infants acquire visual classes in different orders |
We show that in a variety of large-scale deep learning scenarios the gradient dynamically converges to a very small subspace after a short period of training. The subspace is spanned by a few top eigenvectors of the Hessian (equal to the number of classes in the dataset), and is mostly preserved over long periods of tr... | For classification problems with k classes, we show that the gradient tends to live in a tiny, slowly-evolving subspace spanned by the eigenvectors corresponding to the k-largest eigenvalues of the Hessian. |
Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question. This paper introduces a new graph-based recurrent retrieval approach that learns to retrieve reasoning paths over the Wik... | Graph-based recurrent retriever that learns to retrieve reasoning paths over Wikipedia Graph outperforms the most recent state of the art on HotpotQA by more than 14 points. |
Stereo matching is one of the important basic tasks in the computer vision field. In recent years, stereo matching algorithms based on deep learning have achieved excellent performance and become the mainstream research direction. Existing algorithms generally use deep convolutional neural networks (DCNNs) to extract m... | We introduced a shallow featrue extraction network with a large receptive field for stereo matching tasks, which uses a simple structure to get better performance. |
We propose a new output layer for deep neural networks that permits the use of logged contextual bandit feedback for training. Such contextual bandit feedback can be available in huge quantities (e.g., logs of search engines, recommender systems) at little cost, opening up a path for training deep networks on orders of... | The paper proposes a new output layer for deep networks that permits the use of logged contextual bandit feedback for training. |
Protein classification is responsible for the biological sequence, we came up with an idea whichdeals with the classification of proteomics using deep learning algorithm. This algorithm focusesmainly to classify sequences of protein-vector which is used for the representation of proteomics.Selection of the type pro... | Protein Family Classification using Deep Learning |
In this work, we attempt to answer a critical question: whether there exists some input sequence that will cause a well-trained discrete-space neural network sequence-to-sequence (seq2seq) model to generate egregious outputs (aggressive, malicious, attacking, etc.). And if such inputs exist, how to find them efficient... | This paper aims to provide an empirical answer to the question of whether well-trained dialogue response model can output malicious responses. |
In the problem of unsupervised learning of disentangled representations, one of the promising methods is to penalize the total correlation of sampled latent vari-ables. Unfortunately, this well-motivated strategy often fail to achieve disentanglement due to a problematic difference between the sampled latent represen... | diagnosed all the problem of STOA VAEs theoretically and qualitatively |
Visual attention mechanisms have been widely used in image captioning models. In this paper, to better link the image structure with the generated text, we replace the traditional softmax attention mechanism by two alternative sparsity-promoting transformations: sparsemax and Total-Variation Sparse Attention (TVmax). W... | We propose a new sparse and structured attention mechanism, TVmax, which promotes sparsity and encourages the weight of related adjacent locations to be the same. |
Although there are more than 65,000 languages in the world, the pronunciations of many phonemes sound similar across the languages. When people learn a foreign language, their pronunciation often reflect their native language's characteristics. That motivates us to investigate how the speech synthesis network learns th... | Learned phoneme embeddings of multilingual neural speech synthesis network could represent relations of phoneme pronunciation between the languages. |
Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, visualization and understanding of GANs is largely missing. How does a GAN represent our visual world... | GAN representations are examined in detail, and sets of representation units are found that control the generation of semantic concepts in the output. |
Historically, the pursuit of efficient inference has been one of the driving forces be-hind the research into new deep learning architectures and building blocks. Some of the recent examples include: the squeeze-and-excitation module of (Hu et al.,2018), depthwise separable convolutions in Xception (Chollet, 2017), an... | Sparse MobileNets are faster than Dense ones with the appropriate kernels. |
In this work, we address the semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Recent works often solve this problem with the advanced graph convolution in a conventional supervised manner, but the performance could b... | We propose a novel graph inference learning framework by building structure relations to infer unknown node labels from those labeled nodes in an end-to-end way. |
This paper proposes a method for efficient training of Q-function for continuous-state Markov Decision Processes (MDP), such that the traces of the resulting policies satisfy a Linear Temporal Logic (LTL) property. LTL, a modal logic, can express a wide range of time-dependent logical properties including safety and li... | As safety is becoming a critical notion in machine learning we believe that this work can act as a foundation for a number of research directions such as safety-aware learning algorithms. |
We introduce two approaches for conducting efficient Bayesian inference in stochastic simulators containing nested stochastic sub-procedures, i.e., internal procedures for which the density cannot be calculated directly such as rejection sampling loops. The resulting class of simulators are used extensively throughout ... | We introduce two approaches for efficient and scalable inference in stochastic simulators for which the density cannot be evaluated directly due to, for example, rejection sampling loops. |
While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary). Previous work has studied this tradeoff between standard and robust accuracy, but only in the setting where no predictor performs well on both objectives in the infinite data... | Even if there is no tradeoff in the infinite data limit, adversarial training can have worse standard accuracy even in a convex problem. |
Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency. In particular, the recent DenseNet is efficient in computation and parameters, and achieves state-of-the-art predictions by directly connecting each feature layer to all previous ones. However, DenseNet's extreme... | We show shortcut connections should be placed in patterns that minimize between-layer distances during backpropagation, and design networks that achieve log L distances using L log(L) connections. |
Building agents to interact with the web would allow for significant improvements in knowledge understanding and representation learning. However, web navigation tasks are difficult for current deep reinforcement learning (RL) models due to the large discrete action space and the varying number of actions between the s... | Graph-based Deep Q Network for Web Navigation |
Learning disentangled representations that correspond to factors of variation in real-world data is critical to interpretable and human-controllable machine learning. Recently, concerns about the viability of learning disentangled representations in a purely unsupervised manner has spurred a shift toward the incorporat... | We construct a theoretical framework for weakly supervised disentanglement and conducted lots of experiments to back up the theory. |
Despite the growing interest in continual learning, most of its contemporary works have been studied in a rather restricted setting where tasks are clearly distinguishable, and task boundaries are known during training. However, if our goal is to develop an algorithm that learns as humans do, this setting is far from r... | We propose an expansion-based approach for task-free continual learning for the first time. Our model consists of a set of neural network experts and expands the number of experts under the Bayesian nonparametric principle. |
Stochastic Gradient Descent (SGD) with Nesterov's momentum is a widely used optimizer in deep learning, which is observed to have excellent generalization performance. However, due to the large stochasticity, SGD with Nesterov's momentum is not robust, i.e., its performance may deviate significantly from the expectatio... | Amortizing Nesterov's momentum for more robust, lightweight and fast deep learning training. |
A state-of-the-art generative model, a ”factorized action variational autoencoder (FAVAE),” is presented for learning disentangled and interpretable representations from sequential data via the information bottleneck without supervision. The purpose of disentangled representation learning is to obtain interpretable and... | We propose new model that can disentangle multiple dynamic factors in sequential data |
Generative networks are promising models for specifying visual transformations. Unfortunately, certification of generative models is challenging as one needs to capture sufficient non-convexity so to produce precise bounds on the output. Existing verification methods either fail to scale to generative networks or do no... | We verify deterministic and probabilistic properties of neural networks using non-convex relaxations over visible transformations specified by generative models |
Multi-view video summarization (MVS) lacks researchers’ attention due to their major challenges of inter-view correlations and overlapping of cameras. Most of the prior MVS works are offline, relying on only summary, needing extra communication bandwidth and transmission time with no focus on uncertain environments. Di... | An efficient multi-view video summarization scheme advanced to activity recognition in IoT environments. |
A central goal in the study of the primate visual cortex and hierarchical models for object recognition is understanding how and why single units trade off invariance versus sensitivity to image transformations. For example, in both deep networks and visual cortex there is substantial variation from layer-to-layer and ... | Rectification in deep neural networks naturally leads them to favor an invariant representation. |
We study the use of the Wave-U-Net architecture for speech enhancement, a model introduced by Stoller et al for the separation of music vocals and accompaniment. This end-to-end learning method for audio source separation operates directly in the time domain, permitting the integrated modelling of phase information a... | The Wave-U-Net architecture, recently introduced by Stoller et al for music source separation, is highly effective for speech enhancement, beating the state of the art. |
Reinforcement learning (RL) is a powerful framework for solving problems by exploring and learning from mistakes. However, in the context of autonomous vehicle (AV) control, requiring an agent to make mistakes, or even allowing mistakes, can be quite dangerous and costly in the real world. For this reason, AV RL is gen... | We introduce a novel framework for learning from demonstration that uses continuous human feedback; we evaluate this framework on continuous control for autonomous vehicles. |
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple feed-forward encoder and d... | scale and enhance VQ-VAE with powerful priors to generate near realistic images. |
We present a graph neural network assisted Monte Carlo Tree Search approach for the classical traveling salesman problem (TSP). We adopt a greedy algorithm framework to construct the optimal solution to TSP by adding the nodes successively. A graph neural network (GNN) is trained to capture the local and global graph s... | A Graph Neural Network Assisted Monte Carlo Tree Search Approach to Traveling Salesman Problem |
Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes) and not the spatial direction from one atom to another. However, directional information plays a central role ... | Directional message passing incorporates spatial directional information to improve graph neural networks. |
Training an agent to solve control tasks directly from high-dimensional images with model-free reinforcement learning (RL) has proven difficult. The agent needs to learn a latent representation together with a control policy to perform the task. Fitting a high-capacity encoder using a scarce reward signal is not only ... | We design a simple and efficient model-free off-policy method for image-based reinforcement learning that matches the state-of-the-art model-based methods in sample efficiency |
Large deep neural networks require huge memory to run and their running speed is sometimes too slow for real applications. Therefore network size reduction with keeping accuracy is crucial for practical applications. We present a novel neural network operator, chopout, with which neural networks are trained, even in a... | We present a novel simple operator, chopout, with which neural networks are trained, even in a single training process, so as to truncated sub-networks perform as well as possible. |
Generative Adversarial Networks (GANs) have been shown to produce realistically looking synthetic images with remarkable success, yet their performance seems less impressive when the training set is highly diverse. In order to provide a better fit to the target data distribution when the dataset includes many different... | Multi modal Guassian distribution of latent space in GAN models improves performance and allows to trade-off quality vs. diversity |
Distributed optimization is essential for training large models on large datasets. Multiple approaches have been proposed to reduce the communication overhead in distributed training, such as synchronizing only after performing multiple local SGD steps, and decentralized methods (e.g., using gossip algorithms) to decou... | SlowMo improves the optimization and generalization performance of communication-efficient decentralized algorithms without sacrificing speed. |
Structural planning is important for producing long sentences, which is a missing part in current language generation models. In this work, we add a planning phase in neural machine translation to control the coarse structure of output sentences. The model first generates some planner codes, then predicts real output w... | Plan the syntactic structural of translation using codes |
Interpolation of data in deep neural networks has become a subject of significant research interest. We prove that over-parameterized single layer fully connected autoencoders do not merely interpolate, but rather, memorize training data: they produce outputs in (a non-linear version of) the span of the training exam... | We identify memorization as the inductive bias of interpolation in overparameterized fully connected and convolutional auto-encoders. |
The tremendous success of deep neural networks has motivated the need to better understand the fundamental properties of these networks, but many of the theoretical results proposed have only been for shallow networks. In this paper, we study an important primitive for understanding the meaningful input space of a deep... | We provably recover the span of a deep multi-layered neural network with latent structure and empirically apply efficient span recovery algorithms to attack networks by obfuscating inputs. |
In this paper, we study the implicit regularization of the gradient descent algorithm in homogeneous neural networks, including fully-connected and convolutional neural networks with ReLU or LeakyReLU activations. In particular, we study the gradient descent or gradient flow (i.e., gradient descent with infinitesimal s... | We study the implicit bias of gradient descent and prove under a minimal set of assumptions that the parameter direction of homogeneous models converges to KKT points of a natural margin maximization problem. |
Long-term video prediction is highly challenging since it entails simultaneously capturing spatial and temporal information across a long range of image frames.Standard recurrent models are ineffective since they are prone to error propagation and cannot effectively capture higher-order correlations. A potential soluti... | we propose convolutional tensor-train LSTM, which learns higher-order Convolutional LSTM efficiently using convolutional tensor-train decomposition. |
While deep neural networks have shown outstanding results in a wide range of applications,
learning from a very limited number of examples is still a challenging
task. Despite the difficulties of the few-shot learning, metric-learning techniques
showed the potential of the neural networks for this task. While these ... | The proposed method is an end-to-end neural SVM, which is optimized for few-shot learning. |
Most existing 3D CNN structures for video representation learning are clip-based methods, and do not consider video-level temporal evolution of spatio-temporal features. In this paper, we propose Video-level 4D Convolutional Neural Networks, namely V4D, to model the evolution of long-range spatio-temporal representatio... | A novel 4D CNN structure for video-level representation learning, surpassing recent 3D CNNs. |
We study the problem of learning and optimizing through physical simulations via differentiable programming. We present DiffSim, a new differentiable programming language tailored for building high-performance differentiable physical simulations. We demonstrate the performance and productivity of our language in gradie... | We study the problem of learning and optimizing through physical simulations via differentiable programming, using our proposed DiffSim programming language and compiler. |
Local explanation frameworks aim to rationalize particular decisions made by a black-box prediction model. Existing techniques are often restricted to a specific type of predictor or based on input saliency, which may be undesirably sensitive to factors unrelated to the model's decision making process. We instead propo... | We present a method for interpreting black-box models by using instance-wise backward selection to identify minimal subsets of features that alone suffice to justify a particular decision made by the model. |
In this paper, we tackle the problem of detecting samples that are not drawn from the training distribution, i.e., out-of-distribution (OOD) samples, in classification. Many previous studies have attempted to solve this problem by regarding samples with low classification confidence as OOD examples using deep neural ne... | We propose a method that extracts the uncertainties of features in each layer of DNNs and combines them for detecting OOD samples when solving classification tasks. |
In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised dat... | We leverage deterministic autoencoders as generative models by proposing mixing functions which combine hidden states from pairs of images. These mixes are made to look realistic through an adversarial framework. |
We outline the problem of concept drifts for time series data. In this work, we analyze the temporal inconsistency of streaming wireless signals in the context of device-free passive indoor localization. We show that data obtained from WiFi channel state information (CSI) can be used to train a robust system capable of... | We introduce an augmented robust feature space for streaming wifi data that is capable of tackling concept drift for indoor localization |
Federated learning improves data privacy and efficiency in machine learning performed over networks of distributed devices, such as mobile phones, IoT and wearable devices, etc. Yet models trained with federated learning can still fail to generalize to new devices due to the problem of domain shift. Domain shift occurs... | we present a principled approach to the problem of federated domain adaptation, which aims to align the representations learned among the different nodes with the data distribution of the target node. |
A capsule is a group of neurons whose outputs represent different properties of the same entity. Each layer in a capsule network contains many capsules. We describe a version of capsules in which each capsule has a logistic unit to represent the presence of an entity and a 4x4 matrix which could learn to represent the ... | Capsule networks with learned pose matrices and EM routing improves state of the art classification on smallNORB, improves generalizability to new view points, and white box adversarial robustness. |
We study a general formulation of program synthesis called syntax-guided synthesis(SyGuS) that concerns synthesizing a program that follows a given grammar and satisfies a given logical specification. Both the logical specification and the grammar have complex structures and can vary from task to task, posing significa... | We propose a meta-learning framework that learns a transferable policy from only weak supervision to solve synthesis tasks with different logical specifications and grammars. |
Simultaneous machine translation models start generating a target sequence before they have encoded or read the source sequence. Recent approach for this task either apply a fixed policy on transformer, or a learnable monotonic attention on a weaker recurrent neural network based structure. In this paper, we propose a ... | Make the transformer streamable with monotonic attention. |
This paper presents a novel two-step approach for the fundamental problem of learning an optimal map from one distribution to another. First, we learn an optimal transport (OT) plan, which can be thought as a one-to-many map between the two distributions. To that end, we propose a stochastic dual approach of regularize... | Learning optimal mapping with deepNN between distributions along with theoretical guarantees. |
Learning effective text representations is a key foundation for numerous machine learning and NLP applications. While the celebrated Word2Vec technique yields semantically rich word representations, it is less clear whether sentence or document representations should be built upon word representations or from scratch. ... | A novel approach to building an unsupervised document (sentence) embeddings from pre-trainedword embeddings |
We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate the probability of the event that the property is violated under an input model. Our approach critically varies from the formal verification f... | We introduce a statistical approach to assessing neural network robustness that provides an informative notion of how robust a network is, rather than just the conventional binary assertion of whether or not of property is violated. |
Recent pretrained transformer-based language models have set state-of-the-art performances on various NLP datasets. However, despite their great progress, they suffer from various structural and syntactic biases. In this work, we investigate the lexical overlap bias, e.g., the model classifies two sentences that have a... | Enhancing the robustness of pretrained transformer models against the lexical overlap bias by extending the input sentences of the training data with their corresponding predicate-argument structures |
We propose a method for quantifying uncertainty in neural network regression models when the targets are real values on a $d$-dimensional simplex, such as probabilities. We show that each target can be modeled as a sample from a Dirichlet distribution, where the parameters of the Dirichlet are provided by the output of... | Neural network regression should use Dirichlet output distribution when targets are probabilities in order to quantify uncertainty of predictions. |
Deep neural networks have achieved outstanding performance in many real-world applications with the expense of huge computational resources. The DenseNet, one of the recently proposed neural network architecture, has achieved the state-of-the-art performance in many visual tasks. However, it has great redundancy due to... | Learning to Search Efficient DenseNet with Layer-wise Pruning |
We consider the setting of an agent with a fixed body interacting with an unknown and uncertain external world. We show that models trained to predict proprioceptive information about the agent's body come to represent objects in the external world. In spite of being trained with only internally available signals, thes... | We train predictive models on proprioceptive information and show they represent properties of external objects. |
Inspired by the success of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs. The hierarchical architecture consisting of multiple GANs preserves both local and global topo... | A GAN based method to learn important topological features of an arbitrary input graph. |
In Chinese societies, superstition is of paramount importance, and vehicle license plates with desirable numbers can fetch very high prices in auctions. Unlike other valuable items, license plates are not allocated an estimated price before auction.
I propose that the task of predicting plate prices can be viewed as... | Predicting auction price of vehicle license plates in Hong Kong with deep recurrent neural network, based on the characters on the plates. |
We present a new latent model of natural images that can be learned on large-scale datasets. The learning process provides a latent embedding for every image in the training dataset, as well as a deep convolutional network that maps the latent space to the image space. After training, the new model provides a strong an... | We present a new deep latent model of natural images that can be trained from unlabeled datasets and can be utilized to solve various image restoration tasks. |
Automatic Essay Scoring (AES) has been an active research area as it can greatly reduce the workload of teachers and prevents subjectivity bias . Most recent AES solutions apply deep neural network (DNN)-based models with regression, where the neural neural-based encoder learns an essay representation that helps differ... | Automatically score essays on sparse data by comparing new essays with known samples with Referee Network. |
One of the fundamental tasks in understanding genomics is the problem of predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds of Transcription Factors (TFs) as labels, genomic-sequence based TFBS prediction is a challenging multi-label classification task. There are two major biological mechan... | We combine the matching network framework for few shot learning into a large scale multi-label model for genomic sequence classification. |
Previous work shows that adversarially robust generalization requires larger sample complexity, and the same dataset, e.g., CIFAR-10, which enables good standard accuracy may not suffice to train robust models. Since collecting new training data could be costly, we focus on better utilizing the given data by inducing t... | Applying the softmax function in training leads to indirect and unexpected supervision on features. We propose a new training objective to explicitly induce dense feature regions for locally sufficient samples to benefit adversarial robustness. |
We present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts with a segmentation-based network dissection method. Then, we examine the causal effect of interpretable units by measuri... | GAN representations are examined in detail, and sets of representation units are found that control the generation of semantic concepts in the output. |
We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an ``incomplete'' signal such as a low-resolution image, a surface normal map, or edges. Current state-of-the-art deep generative models designed for such conditional image synthesis lack two important things: ... | Pixel-wise nearest neighbors used for generating multiple images from incomplete priors such as a low-res images, surface normals, edges etc. |
Neural networks are vulnerable to adversarial examples and researchers have proposed many heuristic attack and defense mechanisms. We address this problem through the principled lens of distributionally robust optimization, which guarantees performance under adversarial input perturbations. By considering a Lagrangia... | We provide a fast, principled adversarial training procedure with computational and statistical performance guarantees. |
Amortized inference has led to efficient approximate inference for large datasets. The quality of posterior inference is largely determined by two factors: a) the ability of the variational distribution to model the true posterior and b) the capacity of the recognition network to generalize inference over all datapoint... | We decompose the gap between the marginal log-likelihood and the evidence lower bound and study the effect of the approximate posterior on the true posterior distribution in VAEs. |
In this paper, we propose a framework that leverages semi-supervised models to improve unsupervised clustering performance. To leverage semi-supervised models, we first need to automatically generate labels, called pseudo-labels. We find that prior approaches for generating pseudo-labels hurt clustering performance bec... | Using ensembles and pseudo labels for unsupervised clustering |
This paper concerns dictionary learning, i.e., sparse coding, a fundamental representation learning problem. We show that a subgradient descent algorithm, with random initialization, can recover orthogonal dictionaries on a natural nonsmooth, nonconvex L1 minimization formulation of the problem, under mild statistical ... | Efficient dictionary learning by L1 minimization via a novel analysis of the non-convex non-smooth geometry. |
We study model recovery for data classification, where the training labels are generated from a one-hidden-layer fully -connected neural network with sigmoid activations, and the goal is to recover the weight vectors of the neural network. We prove that under Gaussian inputs, the empirical risk function using cross ent... | We provide the first theoretical analysis of guaranteed recovery of one-hidden-layer neural networks under cross entropy loss for classification problems. |
With the deployment of neural networks on mobile devices and the necessity of transmitting neural networks over limited or expensive channels, the file size of trained model was identified as bottleneck. We propose a codec for the compression
of neural networks which is based on transform coding for convolutional and ... | Our neural network codec (which is based on transform coding and clustering) enables a low complexity and high efficient transparent compression of neural networks. |
As Machine Learning (ML) gets applied to security-critical or sensitive domains, there is a growing need for integrity and privacy for outsourced ML computations. A pragmatic solution comes from Trusted Execution Environments (TEEs), which use hardware and software protections to isolate sensitive computations from the... | We accelerate secure DNN inference in trusted execution environments (by a factor 4x-20x) by selectively outsourcing the computation of linear layers to a faster yet untrusted co-processor. |
While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements. Consequently, model size reduction has become an utmost goal in deep learning. A typical approach is to train a set of determini... | This paper proposes an effective method to compress neural networks based on recent results in information theory. |
Most existing neural networks for learning graphs deal with the issue of permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors. We argue that this imposes a limitation on their representation power, and instead propose a new g... | A general framework for creating covariant graph neural networks |
In recent years, three-dimensional convolutional neural network (3D CNN) are intensively applied in the video analysis and action recognition and receives good performance. However, 3D CNN leads to massive computation and storage consumption, which hinders its deployment on mobile and embedded devices. In this paper, ... | In this paper, we propose a three-dimensional regularization-based pruning method to accelerate the 3D-CNN. |
In this paper, we propose data statements as a design solution and professional practice for natural language processing technologists, in both research and development — through the adoption and widespread use of data statements, the field can begin to address critical scientific and ethical issues that result from th... | A practical proposal for more ethical and responsive NLP technology, operationalizing transparency of test and training data |
We introduce CGNN, a framework to learn functional causal models as generative neural networks. These networks are trained using backpropagation to minimize the maximum mean discrepancy to the observed data. Unlike previous approaches, CGNN leverages both conditional independences and distributional asymmetries to seam... | Discover the structure of functional causal models with generative neural networks |
Network pruning is widely used for reducing the heavy computational cost of deep models. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning. In this work, we make a rather surprising observation: fine-tuning a pruned model only gives comparable or even worse p... | In network pruning, fine-tuning a pruned model only gives comparable or worse performance than training it from scratch. This advocate a rethinking of existing pruning algorithms. |
Clustering is the central task in unsupervised learning and data mining. k-means is one of the most widely used clustering algorithms. Unfortunately, it is generally non-trivial to extend k-means to cluster data points beyond Gaussian distribution, particularly, the clusters with non-convex shapes (Beliakov & King, 200... | This paper introduces Extreme Value Theory into k-means to measure similarity and proposes a novel algorithm called Extreme Value k-means for clustering. |
Deep reinforcement learning algorithms have proven successful in a variety of domains. However, tasks with sparse rewards remain challenging when the state space is large. Goal-oriented tasks are among the most typical problems in this domain, where a reward can only be received when the final goal is accomplished. In ... | We propose Tendency RL to efficiently solve goal-oriented tasks with large state space using automated curriculum learning and discriminative shaping reward, which has the potential to tackle robot manipulation tasks with perception. |
Human scene perception goes beyond recognizing a collection of objects and their pairwise relations. We understand higher-level, abstract regularities within the scene such as symmetry and repetition. Current vision recognition modules and scene representations fall short in this dimension. In this paper, we present sc... | We present scene programs, a structured scene representation that captures both low-level object appearance and high-level regularity in the scene. |
Modern neural networks often require deep compositions of high-dimensional nonlinear functions (wide architecture) to achieve high test accuracy, and thus can have overwhelming number of parameters. Repeated high cost in prediction at test-time makes neural networks ill-suited for devices with constrained memory or com... | Compression of neural networks which improves the state-of-the-art low rank approximation techniques and is complementary to most of other compression techniques. |
With the rise in employment of deep learning methods in safety-critical scenarios, interpretability is more essential than ever before. Although many different directions regarding interpretability have been explored for visual modalities, time-series data has been neglected with only a handful of methods tested due to... | We present an attribution technique leveraging sparsity inducing norms to achieve interpretability. |
Variance reduction methods which use a mixture of large and small batch gradients, such as SVRG (Johnson & Zhang, 2013) and SpiderBoost (Wang et al., 2018), require significantly more computational resources per update than SGD (Robbins & Monro, 1951). We reduce the computational cost per update of variance reduction m... | We use sparsity to improve the computational complexity of variance reduction methods. |
Despite promising progress on unimodal data imputation (e.g. image inpainting), models for multimodal data imputation are far from satisfactory. In this work, we propose variational selective autoencoder (VSAE) for this task. Learning only from partially-observed data, VSAE can model the joint distribution of observed/... | We propose a novel VAE-based framework learning from partially-observed data for imputation and generation. |
In many domains, especially enterprise text analysis, there is an abundance of data which can be used for the development of new AI-powered intelligent experiences to improve people's productivity. However, there are strong-guarantees of privacy which prevent broad sampling and labeling of personal text data to learn ... | Insights on the domain adaptation challenge, when predicting user intent in enterprise email. |
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