query stringlengths 273 149k | pos stringlengths 18 667 | idx int64 0 1.99k | task_name stringclasses 1
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There are many differences between convolutional networks and the ventral visual streams of primates. For example, standard convolutional networks lack recurrent and lateral connections, cell dynamics, etc. However, their feedforward architectures are somewhat similar to the ventral stream, and warrant a more detailed ... | An approximation of primate ventral stream as a convolutional network performs poorly on object recognition, and multiple architectural features contribute to this. | 1,900 | scitldr |
In reinforcement learning, it is common to let an agent interact with its environment for a fixed amount of time before resetting the environment and repeating the process in a series of episodes. The task that the agent has to learn can either be to maximize its performance over (i) that fixed amount of time, or (ii) ... | We consider the problem of learning optimal policies in time-limited and time-unlimited domains using time-limited interactions. | 1,901 | scitldr |
Although stochastic gradient descent (SGD) is a driving force behind the recent success of deep learning, our understanding of its dynamics in a high-dimensional parameter space is limited. In recent years, some researchers have used the stochasticity of minibatch gradients, or the signal-to-noise ratio, to better char... | One of theoretical issues in deep learning | 1,902 | scitldr |
Design of reliable systems must guarantee stability against input perturbations. In machine learning, such guarantee entails preventing overfitting and ensuring robustness of models against corruption of input data. In order to maximize stability, we analyze and develop a computationally efficient implementation of Jac... | We analyze and develop a computationally efficient implementation of Jacobian regularization that increases the classification margins of neural networks. | 1,903 | scitldr |
With the increasing demand to deploy convolutional neural networks (CNNs) on mobile platforms, the sparse kernel approach was proposed, which could save more parameters than the standard convolution while maintaining accuracy. However, despite the great potential, no prior research has pointed out how to craft an spars... | We are the first in the field to show how to craft an effective sparse kernel design from three aspects: composition, performance and efficiency. | 1,904 | scitldr |
In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions. To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the ... | A novel marginalized average attentional network for weakly-supervised temporal action localization | 1,905 | scitldr |
Deep image prior (DIP), which utilizes a deep convolutional network (ConvNet) structure itself as an image prior, has attracted huge attentions in computer vision community. It empirically shows the effectiveness of ConvNet structure for various image restoration applications. However, why the DIP works so well is stil... | We propose a new auto-encoder incorporated with multiway delay-embedding transform toward interpreting deep image prior. | 1,906 | scitldr |
Federated learning is a recent advance in privacy protection. In this context, a trusted curator aggregates parameters optimized in decentralized fashion by multiple clients. The ing model is then distributed back to all clients, ultimately converging to a joint representative model without explicitly having to share t... | Ensuring that models learned in federated fashion do not reveal a client's participation. | 1,907 | scitldr |
Employing deep neural networks as natural image priors to solve inverse problems either requires large amounts of data to sufficiently train expressive generative models or can succeed with no data via untrained neural networks. However, very few works have considered how to interpolate between these no- to high-data r... | We show how pre-training an untrained neural network with as few as 5-25 examples can improve reconstruction results in compressed sensing and semantic recovery problems like colorization. | 1,908 | scitldr |
We propose Cooperative Training (CoT) for training generative models that measure a tractable density for discrete data. CoT coordinately trains a generator G and an auxiliary predictive mediator M. The training target of M is to estimate a mixture density of the learned distribution G and the target distribution P, an... | We proposed Cooperative Training, a novel training algorithm for generative modeling of discrete data. | 1,909 | scitldr |
Intrinsic rewards in reinforcement learning provide a powerful algorithmic capability for agents to learn how to interact with their environment in a task-generic way. However, increased incentives for motivation can come at the cost of increased fragility to stochasticity. We introduce a method for computing an intrin... | We introduce a method for computing an intrinsic reward for curiosity using metrics derived from sampling a latent variable model used to estimate dynamics. | 1,910 | scitldr |
Word embedding is a powerful tool in natural language processing. In this paper we consider the problem of word embedding composition \--- given vector representations of two words, compute a vector for the entire phrase. We give a generative model that can capture specific syntactic relations between words. Under our ... | We present a generative model for compositional word embeddings that captures syntactic relations, and provide empirical verification and evaluation. | 1,911 | scitldr |
Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI. This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities. We propose a hybrid ... | We propose a hybrid model-based & model-free approach using semantic information to improve DRL generalization in man-made environments. | 1,912 | scitldr |
In this paper, we focus on two challenges which offset the promise of sparse signal representation, sensing, and recovery. First, real-world signals can seldom be described as perfectly sparse vectors in a known basis, and traditionally used random measurement schemes are seldom optimal for sensing them. Second, existi... | We use deep learning techniques to solve the sparse signal representation and recovery problem. | 1,913 | scitldr |
To select effective actions in complex environments, intelligent agents need to generalize from past experience. World models can represent knowledge about the environment to facilitate such generalization. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, ther... | We present Dreamer, an agent that learns long-horizon behaviors purely by latent imagination using analytic value gradients. | 1,914 | scitldr |
Transfer reinforcement learning (RL) aims at improving learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks. However, it remains challenging to transfer knowledge between different environmental dynamics without having access to the source environments. In this work... | We propose MULTIPOLAR, a transfer RL method that leverages a set of source policies collected under unknown diverse environmental dynamics to efficiently learn a target policy in another dynamics. | 1,915 | scitldr |
Reinforcement learning algorithms rely on carefully engineered rewards from the environment that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is difficult and not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosit... | An agent trained only with curiosity, and no extrinsic reward, does surprisingly well on 54 popular environments, including the suite of Atari games, Mario etc. | 1,916 | scitldr |
This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness). Evaluated on these adversarially transformed examples, we demonstrate that adding regularization on top of standard or adversarial tra... | for spatial transformations robust minimizer also minimizes standard accuracy; invariance-inducing regularization leads to better robustness than specialized architectures | 1,917 | scitldr |
We propose order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes. To this end, we design a pairwise comparator to categorize the relationship between two instances into one of three cases: one instance is `greater than,' `simila... | The notion of order learning is proposed and it is applied to regression problems in computer vision | 1,918 | scitldr |
We study how the topology of a data set comprising two components representing two classes of objects in a binary classification problem changes as it passes through the layers of a well-trained neural network, i.e., one with perfect accuracy on training set and a generalization error of less than 1%. The goal is to sh... | We show that neural networks operate by changing topologly of a data set and explore how architectural choices effect this change. | 1,919 | scitldr |
The convergence rate and final performance of common deep learning models have significantly benefited from recently proposed heuristics such as learning rate schedules, knowledge distillation, skip connections and normalization layers. In the absence of theoretical underpinnings, controlled experiments aimed at explai... | We use empirical tools of mode connectivity and SVCCA to investigate neural network training heuristics of learning rate restarts, warmup and knowledge distillation. | 1,920 | scitldr |
The increasing demand for neural networks (NNs) being employed on embedded devices has led to plenty of research investigating methods for training low precision NNs. While most methods involve a quantization step, we propose a principled Bayesian approach where we first infer a distribution over a discrete weight spac... | Variational Inference for infering a discrete distribution from which a low-precision neural network is derived | 1,921 | scitldr |
Many irregular domains such as social networks, financial transactions, neuron connections, and natural language structures are represented as graphs. In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs. However, in many of ... | We propose a novel tensor based method for graph convolutional networks on dynamic graphs | 1,922 | scitldr |
Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications. This task can be formulated as a combinatorial problem, and it takes many hours of human experts to construct, and to evaluate new data. Unsupervised learning me... | "Generating new chemical materials using novel cross-domain GANs." | 1,923 | scitldr |
Given samples from a group of related regression tasks, a data-enriched model describes observations by a common and per-group individual parameters. In high-dimensional regime, each parameter has its own structure such as sparsity or group sparsity. In this paper, we consider the general form of data enrichment where ... | We provide an estimator and an estimation algorithm for a class of multi-task regression problem and provide statistical and computational analysis.. | 1,924 | scitldr |
Autonomous vehicles are becoming more common in city transportation. Companies will begin to find a need to teach these vehicles smart city fleet coordination. Currently, simulation based modeling along with hand coded rules dictate the decision making of these autonomous vehicles. We believe that complex intelligent b... | Utilized Deep Reinforcement Learning to teach agents ride-sharing fleet style coordination. | 1,925 | scitldr |
Stability is a key aspect of data analysis. In many applications, the natural notion of stability is geometric, as illustrated for example in computer vision. Scattering transforms construct deep convolutional representations which are certified stable to input deformations. This stability to deformations can be interp... | Stability of scattering transform representations of graph data to deformations of the underlying graph support. | 1,926 | scitldr |
We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an onl... | We propose a novel deep network architecture that can dynamically decide its network capacity as it trains on a lifelong learning scenario. | 1,927 | scitldr |
This paper fosters the idea that deep learning methods can be sided to classical visual odometry pipelines to improve their accuracy and to produce uncertainty models to their estimations. We show that the biases inherent to the visual odom- etry process can be faithfully learnt and compensated for, and that a learning... | This paper discusses different methods of pairing VO with deep learning and proposes a simultaneous prediction of corrections and uncertainty. | 1,928 | scitldr |
Building robust online content recommendation systems requires learning com- plex interactions between user preferences and content features. The field has evolved rapidly in recent years from traditional multi-arm bandit and collabora- tive filtering techniques, with new methods integrating Deep Learning models that e... | We have introduced Deep Density Network, a unified DNN model to estimate uncertainty for exploration/exploitation in recommendation systems. | 1,929 | scitldr |
While it is well-documented that climate change accepters and deniers have become increasingly polarized in the United States over time, there has been no large-scale examination of whether these individuals are prone to changing their opinions as a of natural external occurrences. On the sub-population of Twitter user... | We train RNNs on famous Twitter users to determine whether the general Twitter population is more likely to believe in climate change after a natural disaster. | 1,930 | scitldr |
We study the control of symmetric linear dynamical systems with unknown dynamics and a hidden state. Using a recent spectral filtering technique for concisely representing such systems in a linear basis, we formulate optimal control in this setting as a convex program. This approach eliminates the need to solve the non... | Using a novel representation of symmetric linear dynamical systems with a latent state, we formulate optimal control as a convex program, giving the first polynomial-time algorithm that solves optimal control with sample complexity only polylogarithmic in the time horizon. | 1,931 | scitldr |
Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions. Since their advent, numerous variations of GANs have been introduced in the literature, primarily focusing on utilization of novel loss functions, optimization/regularizat... | We model the data generator (in GAN) by means of a high-order polynomial represented by high-order tensors. | 1,932 | scitldr |
Deep neural networks trained on large supervised datasets have led to impressive in recent years. However, since well-annotated datasets can be prohibitively expensive and time-consuming to collect, recent work has explored the use of larger but noisy datasets that can be more easily obtained. In this paper, we investi... | We show that deep neural networks are able to learn from data that has been diluted by an arbitrary amount of noise. | 1,933 | scitldr |
In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm for low resource neural machine translation (NMT). We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks. We use t... | we propose a meta-learning approach for low-resource neural machine translation that can rapidly learn to translate on a new language | 1,934 | scitldr |
This work presents a method for active anomaly detection which can be built upon existing deep learning solutions for unsupervised anomaly detection. We show that a prior needs to be assumed on what the anomalies are, in order to have performance guarantees in unsupervised anomaly detection. We argue that active anomal... | A method for active anomaly detection. We present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method. | 1,935 | scitldr |
In this paper, we ask for the main factors that determine a classifier's decision making and uncover such factors by studying latent codes produced by auto-encoding frameworks. To deliver an explanation of a classifier's behaviour, we propose a method that provides series of examples highlighting semantic differences b... | We generate examples to explain a classifier desicion via interpolations in latent space. The variational auto encoder cost is extended with a functional of the classifier over the generated example path in data space. | 1,936 | scitldr |
The soundness and optimality of a plan depends on the correctness of the domain model. In real-world applications, specifying complete domain models is difficult as the interactions between the agent and its environment can be quite complex. We propose a framework to learn a PPDDL representation of the model incrementa... | Introduce an approach to allow agents to learn PPDDL action models incrementally over multiple planning problems under the framework of reinforcement learning. | 1,937 | scitldr |
The field of Deep Reinforcement Learning (DRL) has recently seen a surge in the popularity of maximum entropy reinforcement learning algorithms. Their popularity stems from the intuitive interpretation of the maximum entropy objective and their superior sample efficiency on standard benchmarks. In this paper, we seek t... | We propose a new DRL off-policy algorithm achieving state-of-the-art performance. | 1,938 | scitldr |
Very recently, it comes to be a popular approach for answering open-domain questions by first searching question-related passages, then applying reading comprehension models to extract answers. Existing works usually extract answers from single passages independently, thus not fully make use of the multiple searched pa... | We propose a method that can make use of the multiple passages information for open-domain QA. | 1,939 | scitldr |
Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents. Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks. Furthermore, social networks can be ext... | Dimensionality reduction algorithm to visualise text with network information, for example an email corpus or co-authorships. | 1,940 | scitldr |
Machine learned models exhibit bias, often because the datasets used to train them are biased. This presents a serious problem for the deployment of such technology, as the ing models might perform poorly on populations that are minorities within the training set and ultimately present higher risks to them. We propose ... | We present a framework that leverages high-fidelity computer simulations to interrogate and diagnose biases within ML classifiers. | 1,941 | scitldr |
Point clouds are a flexible and ubiquitous way to represent 3D objects with arbitrary resolution and precision. Previous work has shown that adapting encoder networks to match the semantics of their input point clouds can significantly improve their effectiveness over naive feedforward alternatives. However, the vast m... | We present and evaluate sampling-based point cloud decoders that outperform the baseline MLP approach by better matching the semantics of point clouds. | 1,942 | scitldr |
We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline an... | We use deep RL to learn a policy that directs the search of a genetic algorithm to better optimize the execution cost of computation graphs, and show improved results on real-world TensorFlow graphs. | 1,943 | scitldr |
Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction. One of the most basic prediction tasks is view prediction: how would a given scene look from an alternative viewpoint? Humans excel at this task. Our ability to imagine and fill in missing visual inform... | We show that with the right loss and architecture, view-predictive learning improves 3D object detection | 1,944 | scitldr |
The modeling of style when synthesizing natural human speech from text has been the focus of significant attention. Some state-of-the-art approaches train an encoder-decoder network on paired text and audio samples (x_txt, x_aud) by encouraging its output to reconstruct x_aud. The synthesized audio waveform is expected... | a generative adversarial network for style modeling in a text-to-speech system | 1,945 | scitldr |
Empirical evidence suggests that neural networks with ReLU activations generalize better with over-parameterization. However, there is currently no theoretical analysis that explains this observation. In this work, we study a simplified learning task with over-parameterized convolutional networks that empirically exhib... | We show in a simplified learning task that over-parameterization improves generalization of a convnet that is trained with gradient descent. | 1,946 | scitldr |
We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-learning algorithm that implicitly learns a model prior distribution of interest. Our proposed method extends the PAC-Bayes framework from a single task setting to the few-shot meta-learning setting to upper-bound generalisation errors on unseen tasks... | Bayesian meta-learning using PAC-Bayes framework and implicit prior distributions | 1,947 | scitldr |
As the area of Explainable AI (XAI), and Explainable AI Planning (XAIP), matures, the ability for agents to generate and curate explanations will likewise grow. We propose a new challenge area in the form of rebellious and deceptive explanations. We discuss how these explanations might be generated and then briefly dis... | Position paper proposing rebellious and deceptive explanations for agents. | 1,948 | scitldr |
We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features. In general, our superstructure is a tree structure of multiple super latent variables and it is automatically learned from data. When there is only one latent variable in the ... | We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features. | 1,949 | scitldr |
Many practical robot locomotion tasks require agents to use control policies that can be parameterized by goals. Popular deep reinforcement learning approaches in this direction involve learning goal-conditioned policies or value functions, or Inverse Dynamics Models (IDMs). IDMs map an agent’s current state and desire... | We show that the key to achieving good performance with IDMs lies in learning latent representations to encode the information shared between equivalent experiences, so that they can be generalized to unseen scenarios. | 1,950 | scitldr |
In this paper, we first identify \textit{angle bias}, a simple but remarkable phenomenon that causes the vanishing gradient problem in a multilayer perceptron (MLP) with sigmoid activation functions. We then propose \textit{linearly constrained weights (LCW)} to reduce the angle bias in a neural network, so as to train... | We identify angle bias that causes the vanishing gradient problem in deep nets and propose an efficient method to reduce the bias. | 1,951 | scitldr |
Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult. In recent years, graph neural networks (GNNs)... | We employ graph neural networks in the variational EM framework for efficient inference and learning of Markov Logic Networks. | 1,952 | scitldr |
Reinforcement learning (RL) methods achieved major advances in multiple tasks surpassing human performance. However, most of RL strategies show a certain degree of weakness and may become computationally intractable when dealing with high-dimensional and non-stationary environments. In this paper, we build a meta-reinf... | A meta-reinforcement learning approach embedding a neural network controller applied to autonomous driving with Carla simulator. | 1,953 | scitldr |
The information bottleneck principle is an elegant and useful approach to representation learning. In this paper, we investigate the problem of representation learning in the context of reinforcement learning using the information bottleneck framework, aiming at improving the sample efficiency of the learning algorithm... | Derive an information bottleneck framework in reinforcement learning and some simple relevant theories and tools. | 1,954 | scitldr |
A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce a visual interaction environment that allows many types of tasks to be unified in a single... | We propose a neural module approach to continual learning using a unified visual environment with a large action space. | 1,955 | scitldr |
Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine. In this paper, we present a semi-supervised technique that addresses both these issues simultaneously. We learn dense representations from large unlabelled image datasets, t... | We propose a method of using GANs to generate high quality visual rationales to help explain model predictions. | 1,956 | scitldr |
Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization algorithms which rely on search distributions to efficiently optimize a large variety of objective functions. This paper investigates the potential benefits of using highly flexible search distributions in ES algorithms, in contras... | We propose a new algorithm leveraging the expressiveness of Generative Neural Networks to improve Evolutionary Strategies algorithms. | 1,957 | scitldr |
We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR). For compression, we introduce and study a trace norm regularization technique f... | We compress and speed up speech recognition models on embedded devices through a trace norm regularization technique and optimized kernels. | 1,958 | scitldr |
Training activation quantized neural networks involves minimizing a piecewise constant training loss whose gradient vanishes almost everywhere, which is undesirable for the standard back-propagation or chain rule. An empirical way around this issue is to use a straight-through estimator (STE) in the backward pass only,... | We make theoretical justification for the concept of straight-through estimator. | 1,959 | scitldr |
This paper presents GumbelClip, a set of modifications to the actor-critic algorithm, for off-policy reinforcement learning. GumbelClip uses the concepts of truncated importance sampling along with additive noise to produce a loss function enabling the use of off-policy samples. The modified algorithm achieves an incre... | With a set of modifications, under 10 LOC, to A2C you get an off-policy actor-critic that outperforms A2C and performs similarly to ACER. The modifications are large batchsizes, aggressive clamping, and policy "forcing" with gumbel noise. | 1,960 | scitldr |
In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, ... | Generating text using sentence embeddings from Skip-Thought Vectors with the help of Generative Adversarial Networks. | 1,961 | scitldr |
Autoregressive recurrent neural decoders that generate sequences of tokens one-by-one and left-to-right are the workhorse of modern machine translation. In this work, we propose a new decoder architecture that can generate natural language sequences in an arbitrary order. Along with generating tokens from a given vocab... | new out-of-order decoder for neural machine translation | 1,962 | scitldr |
In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Although the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in polynomial time compared to the exponential time of the original NP-complete problem. Different from other ... | In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. | 1,963 | scitldr |
Domain adaptation is an open problem in deep reinforcement learning (RL). Often, agents are asked to perform in environments where data is difficult to obtain. In such settings, agents are trained in similar environments, such as simulators, and are then transferred to the original environment. The gap between visual o... | We present an agent that uses a beta-vae to extract visual features and an attention mechanism to ignore irrelevant features from visual observations to enable robust transfer between visual domains. | 1,964 | scitldr |
Robustness verification that aims to formally certify the prediction behavior of neural networks has become an important tool for understanding the behavior of a given model and for obtaining safety guarantees. However, previous methods are usually limited to relatively simple neural networks. In this paper, we conside... | We propose the first algorithm for verifying the robustness of Transformers. | 1,965 | scitldr |
In the last few years, deep learning has been tremendously successful in many applications. However, our theoretical understanding of deep learning, and thus the ability of providing principled improvements, seems to lag behind. A theoretical puzzle concerns the ability of deep networks to predict well despite their in... | Contrary to previous beliefs, the training performance of deep networks, when measured appropriately, is predictive of test performance, consistent with classical machine learning theory. | 1,966 | scitldr |
We propose a "plan online and learn offline" framework for the setting where an agent, with an internal model, needs to continually act and learn in the world. Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration. We study how local trajector... | We propose a framework that incorporates planning for efficient exploration and learning in complex environments. | 1,967 | scitldr |
Modern neural network architectures take advantage of increasingly deeper layers, and various advances in their structure to achieve better performance. While traditional explicit regularization techniques like dropout, weight decay, and data augmentation are still being used in these new models, little about the regul... | Our paper analyses the tremendous representational power of networks especially with 'skip connections', which may be used as a method for better generalization. | 1,968 | scitldr |
One of the challenges in training generative models such as the variational auto encoder (VAE) is avoiding posterior collapse. When the generator has too much capacity, it is prone to ignoring latent code. This problem is exacerbated when the dataset is small, and the latent dimension is high. The root of the problem i... | This paper proposes a new objective function to replace KL term with one that emulates maximum mean discrepancy (MMD) objective. | 1,969 | scitldr |
Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significant... | We pose that generative models' likelihoods are excessively influenced by the input's complexity, and propose a way to compensate it when detecting out-of-distribution inputs | 1,970 | scitldr |
Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSProp, Adam, Adadelta, Nadam are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients. In many applications, e.g. learning with large ... | We investigate the convergence of popular optimization algorithms like Adam , RMSProp and propose new variants of these methods which provably converge to optimal solution in convex settings. | 1,971 | scitldr |
Targeted clean-label poisoning is a type of adversarial attack on machine learning systems where the adversary injects a few correctly-labeled, minimally-perturbed samples into the training data thus causing the deployed model to misclassify a particular test sample during inference. Although defenses have been propose... | We present effective defenses to clean-label poisoning attacks. | 1,972 | scitldr |
Abstract reasoning, particularly in the visual domain, is a complex human ability, but it remains a challenging problem for artificial neural learning systems. In this work we propose MXGNet, a multilayer graph neural network for multi-panel diagrammatic reasoning tasks. MXGNet combines three powerful concepts, namely,... | MXGNet is a multilayer, multiplex graph based architecture which achieves good performance on various diagrammatic reasoning tasks. | 1,973 | scitldr |
Semantic structure extraction for spreadsheets includes detecting table regions, recognizing structural components and classifying cell types. Automatic semantic structure extraction is key to automatic data transformation from various table structures into canonical schema so as to enable data analysis and knowledge d... | We propose a novel multi-task framework that learns table detection, semantic component recognition and cell type classification for spreadsheet tables with promising results. | 1,974 | scitldr |
Open-domain dialogue generation has gained increasing attention in Natural Language Processing. Comparing these methods requires a holistic means of dialogue evaluation. Human ratings are deemed as the gold standard. As human evaluation is inefficient and costly, an automated substitute is desirable. In this paper, we ... | We propose automatic metrics to holistically evaluate open-dialogue generation and they strongly correlate with human evaluation. | 1,975 | scitldr |
Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending CNNs to the general spatial domain. Although various types of graph convolution and geometric convolutio... | We devise a novel Depthwise Separable Graph Convolution (DSGC) for the generic spatial domain data, which is highly compatible with depthwise separable convolution. | 1,976 | scitldr |
Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales. Fortunately, most music is also highly structured and can be represented as discrete note events played on musical instruments. Herein, we show that by using no... | We train a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure, enabled by the new MAESTRO dataset. | 1,977 | scitldr |
Variational inference based on chi-square divergence minimization (CHIVI) provides a way to approximate a model's posterior while obtaining an upper bound on the marginal likelihood. However, in practice CHIVI relies on Monte Carlo (MC) estimates of an upper bound objective that at modest sample sizes are not guarantee... | An empirical study of variational inference based on chi-square divergence minimization, showing that minimizing the CUBO is trickier than maximizing the ELBO | 1,978 | scitldr |
It has been widely recognized that adversarial examples can be easily crafted to fool deep networks, which mainly root from the locally non-linear behavior nearby input examples. Applying mixup in training provides an effective mechanism to improve generalization performance and model robustness against adversarial per... | We exploit the global linearity of the mixup-trained models in inference to break the locality of the adversarial perturbations. | 1,979 | scitldr |
Fine-tuning language models, such as BERT, on domain specific corpora has proven to be valuable in domains like scientific papers and biomedical text. In this paper, we show that fine-tuning BERT on legal documents similarly provides valuable improvements on NLP tasks in the legal domain. Demonstrating this outcome is ... | Fine-tuning BERT on legal corpora provides marginal, but valuable, improvements on NLP tasks in the legal domain. | 1,980 | scitldr |
We present a deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques. Our probabilistic approach models non-parallel data from two domains as a partially observed parallel corpus. By hypothesizing a parallel latent sequence that generates each observed seque... | We formulate a probabilistic latent sequence model to tackle unsupervised text style transfer, and show its effectiveness across a suite of unsupervised text style transfer tasks. | 1,981 | scitldr |
Current practice in machine learning is to employ deep nets in an overparametrized limit, with the nominal number of parameters typically exceeding the number of measurements. This resembles the situation in compressed sensing, or in sparse regression with $l_1$ penalty terms, and provides a theoretical avenue for unde... | Proposes an analytically tractable model and inference procedure (misparametrized sparse regression, inferred using L_1 penalty and studied in the data-interpolation limit) to study deep-net related phenomena in the context of inverse problems. | 1,982 | scitldr |
Hashing-based collaborative filtering learns binary vector representations (hash codes) of users and items, such that recommendations can be computed very efficiently using the Hamming distance, which is simply the sum of differing bits between two hash codes. A problem with hashing-based collaborative filtering using ... | We propose a new variational hashing-based collaborative filtering approach optimized for a novel self-mask variant of the Hamming distance, which outperforms state-of-the-art by up to 12% on NDCG. | 1,983 | scitldr |
Determining the appropriate batch size for mini-batch gradient descent is always time consuming as it often relies on grid search. This paper considers a resizable mini-batch gradient descent (RMGD) algorithm based on a multi-armed bandit that achieves performance equivalent to that of best fixed batch-size. At each ep... | An optimization algorithm that explores various batch sizes based on probability and automatically exploits successful batch size which minimizes validation loss. | 1,984 | scitldr |
Knowledge graph has gained increasing attention in recent years for its successful applications of numerous tasks. Despite the rapid growth of knowledge construction, knowledge graphs still suffer from severe incompletion and inevitably involve various kinds of errors. Several attempts have been made to complete knowle... | We proposed a unified Generative Adversarial Networks (GAN) framework to learn noise-aware knowledge graph embedding. | 1,985 | scitldr |
Energy-based models (EBMs), a.k.a. un-normalized models, have had recent successes in continuous spaces. However, they have not been successfully applied to model text sequences. While decreasing the energy at training samples is straightforward, mining (negative) samples where the energy should be increased is difficu... | A residual EBM for text whose formulation is equivalent to discriminating between human and machine generated text. We study its generalization behavior. | 1,986 | scitldr |
Semi-supervised learning, i.e. jointly learning from labeled an unlabeled samples, is an active research topic due to its key role on relaxing human annotation constraints. In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods th... | Pseudo-labeling has shown to be a weak alternative for semi-supervised learning. We, conversely, demonstrate that dealing with confirmation bias with several regularizations makes pseudo-labeling a suitable approach. | 1,987 | scitldr |
Model-free reinforcement learning (RL) has been proven to be a powerful, general tool for learning complex behaviors. However, its sample efficiency is often impractically large for solving challenging real-world problems, even for off-policy algorithms such as Q-learning. A limiting factor in classic model-free RL is ... | We show that a special goal-condition value function trained with model free methods can be used within model-based control, resulting in substantially better sample efficiency and performance. | 1,988 | scitldr |
We introduce a neural architecture to perform amortized approximate Bayesian inference over latent random permutations of two sets of objects. The method involves approximating permanents of matrices of pairwise probabilities using recent ideas on functions defined over sets. Each sampled permutation comes with a proba... | A novel neural architecture for efficient amortized inference over latent permutations | 1,989 | scitldr |
Machine learned large-scale retrieval systems require a large amount of training data representing query-item relevance. However, collecting users' explicit feedback is costly. In this paper, we propose to leverage user logs and implicit feedback as auxiliary objectives to improve relevance modeling in retrieval system... | We propose a novel two-tower shared-bottom model architecture for transferring knowledge from rich implicit feedbacks to predict relevance for large-scale retrieval systems. | 1,990 | scitldr |
The ability to autonomously explore and navigate a physical space is a fundamental requirement for virtually any mobile autonomous agent, from household robotic vacuums to autonomous vehicles. Traditional SLAM-based approaches for exploration and navigation largely focus on leveraging scene geometry, but fail to model ... | We address the task of autonomous exploration and navigation using spatial affordance maps that can be learned in a self-supervised manner, these outperform classic geometric baselines while being more sample efficient than contemporary RL algorithms | 1,991 | scitldr |
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