query stringlengths 273 149k | pos stringlengths 18 667 | idx int64 0 1.99k | task_name stringclasses 1
value |
|---|---|---|---|
Fine-grained Entity Recognition (FgER) is the task of detecting and classifying entity mentions to a large set of types spanning diverse domains such as biomedical, finance and sports. We observe that when the type set spans several domains, detection of entity mention becomes a limitation for supervised learning model... | We initiate a push towards building ER systems to recognize thousands of types by providing a method to automatically construct suitable datasets based on the type hierarchy. | 1,400 | scitldr |
Implementing correct method invocation is an important task for software developers. However, this is challenging work, since the structure of method invocation can be complicated. In this paper, we propose InvocMap, a code completion tool allows developers to obtain an implementation of multiple method invocations fro... | This paper proposes a theory of classifying Method Invocations by different abstraction levels and conducting a statistical approach for code completion from method name to method invocation. | 1,401 | scitldr |
Adversaries in neural networks have drawn much attention since their first debut. While most existing methods aim at deceiving image classification models into misclassification or crafting attacks for specific object instances in the object setection tasks, we focus on creating universal adversaries to fool object det... | We focus on creating universal adversaries to fool object detectors and hide objects from the detectors. | 1,402 | scitldr |
This work presents the Poincaré Wasserstein Autoencoder, a reformulation of the recently proposed Wasserstein autoencoder framework on a non-Euclidean manifold, the Poincaré ball model of the hyperbolic space H n. By assuming the latent space to be hyperbolic, we can use its intrinsic hierarchy to impose structure on t... | Wasserstein Autoencoder with hyperbolic latent space | 1,403 | scitldr |
In this paper, we introduce a method to compress intermediate feature maps of deep neural networks (DNNs) to decrease memory storage and bandwidth requirements during inference. Unlike previous works, the proposed method is based on converting fixed-point activations into vectors over the smallest GF finite field follo... | Feature map compression method that converts quantized activations into binary vectors followed by nonlinear dimensionality reduction layers embedded into a DNN | 1,404 | scitldr |
Adversarial training is one of the strongest defenses against adversarial attacks, but it requires adversarial examples to be generated for every mini-batch during optimization. The expense of producing these examples during training often precludes adversarial training from use on complex image datasets. In this study... | Achieving strong adversarial robustness comparable to adversarial training without training on adversarial examples | 1,405 | scitldr |
A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. Typically, this involves minimizing a surrogate objective, such as the negative log likelihood of a generative model, with the hope that representations us... | We learn an unsupervised learning algorithm that produces useful representations from a set of supervised tasks. At test-time, we apply this algorithm to new tasks without any supervision and show performance comparable to a VAE. | 1,406 | scitldr |
There is significant recent evidence in supervised learning that, in the over-parametrized setting, wider networks achieve better test error. In other words, the bias-variance tradeoff is not directly observable when increasing network width arbitrarily. We investigate whether a corresponding phenomenon is present in r... | Over-parametrization in width seems to help in deep reinforcement learning, just as it does in supervised learning. | 1,407 | scitldr |
Learning disentangled representations of data is one of the central themes in unsupervised learning in general and generative modelling in particular. In this work, we tackle a slightly more intricate scenario where the observations are generated from a conditional distribution of some known control variate and some la... | Hierarchical generative model (hybrid of VAE and GAN) that learns a disentangled representation of data without compromising the generative quality. | 1,408 | scitldr |
Deep learning has yielded state-of-the-art performance on many natural language processing tasks including named entity recognition (NER). However, this typically requires large amounts of labeled data. In this work, we demonstrate that the amount of labeled training data can be drastically reduced when deep learning i... | We introduce a lightweight architecture for named entity recognition and carry out incremental active learning, which is able to match state-of-the-art performance with just 25% of the original training data. | 1,409 | scitldr |
Network quantization is a model compression and acceleration technique that has become essential to neural network deployment. Most quantization methods per- form fine-tuning on a pretrained network, but this sometimes in a large loss in accuracy compared to the original network. We introduce a new technique to train q... | We train accurate fully quantized networks using a loss function maximizing full precision model accuracy and minimizing the difference between the full precision and quantized networks. | 1,410 | scitldr |
While much of the work in the design of convolutional networks over the last five years has revolved around the empirical investigation of the importance of depth, filter sizes, and number of feature channels, recent studies have shown that branching, i.e., splitting the computation along parallel but distinct threads ... | In this paper we introduced an algorithm to learn the connectivity of deep multi-branch networks. The approach is evaluated on image categorization where it consistently yields accuracy gains over state-of-the-art models that use fixed connectivity. | 1,411 | scitldr |
Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret. Especially, little is known about how they represent language in their intermediate layers. In an attempt to understand the representatio... | We show that individual units in CNN representations learned in NLP tasks are selectively responsive to natural language concepts. | 1,412 | scitldr |
Applying reinforcement learning (RL) to real-world problems will require reasoning about action-reward correlation over long time horizons. Hierarchical reinforcement learning (HRL) methods handle this by dividing the task into hierarchies, often with hand-tuned network structure or pre-defined subgoals. We propose a n... | We propose a novel HRL framework, in which we formulate the temporal abstraction problem as learning a latent representation of action sequence. | 1,413 | scitldr |
Recurrent neural networks (RNNs) have achieved state-of-the-art performance on many diverse tasks, from machine translation to surgical activity recognition, yet training RNNs to capture long-term dependencies remains difficult. To date, the vast majority of successful RNN architectures alleviate this problem using nea... | We introduce MIST RNNs, which a) exhibit superior vanishing-gradient properties in comparison to LSTM; b) improve performance substantially over LSTM and Clockwork RNNs on tasks requiring very long-term dependencies; and c) are much more efficient than previously-proposed NARX RNNs, with even fewer parameters and opera... | 1,414 | scitldr |
A well-trained model should classify objects with unanimous score for every category. This requires the high-level semantic features should be alike among samples, despite a wide span in resolution, texture, deformation, etc. Previous works focus on re-designing the loss function or proposing new regularization constra... | (Camera-ready version) A feature intertwiner module to leverage features from one accurate set to help the learning of another less reliable set. | 1,415 | scitldr |
Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner. Recently, several frameworks have been developed which enable deep learning to be deployed in this learning scenario. A key modelling decision is to what extent the architecture should be shared across tas... | A continual learning framework which learns to automatically adapt its architecture based on a proposed variational inference algorithm. | 1,416 | scitldr |
High-dimensional data often lie in or close to low-dimensional subspaces. Sparse subspace clustering methods with sparsity induced by L0-norm, such as L0-Sparse Subspace Clustering (L0-SSC), are demonstrated to be more effective than its L1 counterpart such as Sparse Subspace Clustering (SSC). However, these L0-norm ba... | We propose Noisy-DR-L0-SSC (Noisy Dimension Reduction L0-Sparse Subspace Clustering) to efficiently partition noisy data in accordance to their underlying subspace structure. | 1,417 | scitldr |
Mode connectivity provides novel geometric insights on analyzing loss landscapes and enables building high-accuracy pathways between well-trained neural networks. In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel method... | A novel approach using mode connectivity in loss landscapes to mitigate adversarial effects, repair tampered models and evaluate adversarial robustness | 1,418 | scitldr |
Generative adversarial networks (GANs) learn to map samples from a noise distribution to a chosen data distribution. Recent work has demonstrated that GANs are consequently sensitive to, and limited by, the shape of the noise distribution. For example, a single generator struggles to map continuous noise (e.g. a unifor... | A multi-generator GAN framework with an additional network to learn a prior over the input noise. | 1,419 | scitldr |
Recent advances in Generative Adversarial Networks (GANs) – in architectural design, training strategies, and empirical tricks – have led nearly photorealistic samples on large-scale datasets such as ImageNet. In fact, for one model in particular, BigGAN, metrics such as Inception Score or Frechet Inception Distance ne... | BigGANs do not capture the ImageNet data distributions and are only modestly successful for data augmentation. | 1,420 | scitldr |
Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can help to learn models that can improve the user experience on each device. However, the scale and heterogeneity of federated data presents new ... | We present Leaf, a modular benchmarking framework for learning in federated data, with applications to learning paradigms such as federated learning, meta-learning, and multi-task learning. | 1,421 | scitldr |
Understanding object motion is one of the core problems in computer vision. It requires segmenting and tracking objects over time. Significant progress has been made in instance segmentation, but such models cannot track objects, and more crucially, they are unable to reason in both 3D space and time. We propose a new ... | We introduce a new spatio-temporal embedding loss on videos that generates temporally consistent video instance segmentation, even with occlusions and missed detections, using appearance, geometry, and temporal context. | 1,422 | scitldr |
As reinforcement learning continues to drive machine intelligence beyond its conventional boundary, unsubstantial practices in sparse reward environment severely limit further applications in a broader range of advanced fields. Motivated by the demand for an effective deep reinforcement learning algorithm that accommod... | This paper proposes an advanced policy optimization method with hindsight experience for sparse reward reinforcement learning. | 1,423 | scitldr |
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In thi... | We explore how using background knowledge with query reformulation can help retrieve better supporting evidence when answering multiple-choice science questions. | 1,424 | scitldr |
Deep CNNs have achieved state-of-the-art performance for numerous machine learning and computer vision tasks in recent years, but as they have become increasingly deep, the number of parameters they use has also increased, making them hard to deploy in memory-constrained environments and difficult to interpret. Machine... | We compress deep CNNs by reusing a single convolutional layer in an iterative manner, thereby reducing their parameter counts by a factor proportional to their depth, whilst leaving their accuracies largely unaffected | 1,425 | scitldr |
We extend the recent of by a spectral analysis of representations corresponding to kernel and neural embeddings. They showed that in a simple single layer network, the alignment of the labels to the eigenvectors of the corresponding Gram matrix determines both the convergence of the optimization during training as well... | Spectral analysis for understanding how different representations can improve optimization and generalization. | 1,426 | scitldr |
Obtaining high-quality uncertainty estimates is essential for many applications of deep neural networks. In this paper, we theoretically justify a scheme for estimating uncertainties, based on sampling from a prior distribution. Crucially, the uncertainty estimates are shown to be conservative in the sense that they ne... | We provide theoretical support to uncertainty estimates for deep learning obtained fitting random priors. | 1,427 | scitldr |
In this paper, we propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows. The proposed model is capable of mapping any partial data to a multi-modal latent variational distribution. Sampling from such a distribution leads to stochastic imputation. Prelimin... | We propose an arbitrarily-conditioned data imputation framework built upon variational autoencoders and normalizing flows | 1,428 | scitldr |
This paper studies \emph{model inversion attacks}, in which the access to a model is abused to infer information about the training data. Since its first introduction by~\citet{fredrikson2014privacy}, such attacks have raised serious concerns given that training data usually contain sensitive information. Thus far, suc... | We develop a privacy attack that can recover the sensitive input data of a deep net from its output | 1,429 | scitldr |
Latent space based GAN methods and attention based encoder-decoder architectures have achieved impressive in text generation and Unsupervised NMT respectively. Leveraging the two domains, we propose an adversarial latent space based architecture capable of generating parallel sentences in two languages concurrently and... | We present a novel method for Bilingual Text Generation producing parallel concurrent sentences in two languages. | 1,430 | scitldr |
As Artificial Intelligence (AI) becomes an integral part of our life, the development of explainable AI, embodied in the decision-making process of an AI or robotic agent, becomes imperative. For a robotic teammate, the ability to generate explanations to explain its behavior is one of the key requirements of an explai... | We introduce online explanation to consider the cognitive requirement of the human for understanding the generated explanation by the agent. | 1,431 | scitldr |
Deep neural networks use deeper and broader structures to achieve better performance and consequently, use increasingly more GPU memory as well. However, limited GPU memory restricts many potential designs of neural networks. In this paper, we propose a reinforcement learning based variable swapping and recomputation a... | We propose a reinforcement learning based variable swapping and recomputation algorithm to reduce the memory cost. | 1,432 | scitldr |
In vanilla backpropagation (VBP), activation function matters considerably in terms of non-linearity and differentiability. Vanishing gradient has been an important problem related to the bad choice of activation function in deep learning (DL). This work shows that a differentiable activation function is not necessary ... | Iterative temporal differencing with fixed random feedback alignment support spike-time dependent plasticity in vanilla backpropagation for deep learning. | 1,433 | scitldr |
Inspired by the recent successes of deep generative models for Text-To-Speech (TTS) such as WaveNet (van den) and Tacotron , this article proposes the use of a deep generative model tailored for Automatic Speech Recognition (ASR) as the primary acoustic model (AM) for an overall recognition system with a separate langu... | This paper proposes the use of a deep generative acoustic model for automatic speech recognition, combining naturally with other deep sequence-to-sequence modules using Bayes' rule. | 1,434 | scitldr |
Recent studies show that widely used Deep neural networks (DNNs) are vulnerable to the carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate adversarial examples by leveraging the L_p distance for penalizing perturbations. Different defense methods have also been explored to de... | We propose a new approach for generating adversarial examples based on spatial transformation, which produces perceptually realistic examples compared to existing attacks. | 1,435 | scitldr |
Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either discrete or continuous space. Motivated by the project of design Game AI for King of Glory (KOG), one the world’s most popular mobile game, we consider the scenario with the discrete-continuous hybrid action space. To dire... | A DQN and DDPG hybrid algorithm is proposed to deal with the discrete-continuous hybrid action space. | 1,436 | scitldr |
Over the past decade, knowledge graphs became popular for capturing structured domain knowledge. Relational learning models enable the prediction of missing links inside knowledge graphs. More specifically, latent distance approaches model the relationships among entities via a distance between latent representations. ... | A novel method of modelling Knowledge Graphs based on Distance Embeddings and Neural Networks | 1,437 | scitldr |
Improved generative adversarial network (Improved GAN) is a successful method of using generative adversarial models to solve the problem of semi-supervised learning. However, it suffers from the problem of unstable training. In this paper, we found that the instability is mostly due to the vanishing gradients on the g... | Improve Training Stability of Semi-supervised Generative Adversarial Networks with Collaborative Training | 1,438 | scitldr |
It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width. This correspondence enables exact Bayesian inference for infinite width neural networks on regression tasks by means of eva... | We show how to make predictions using deep networks, without training deep networks. | 1,439 | scitldr |
Search space is a key consideration for neural architecture search. Recently, Xie et al. (2019a) found that randomly generated networks from the same distribution perform similarly, which suggest we should search for random graph distributions instead of graphs. We propose graphon as a new search space. A graphon is th... | Graphon is a good search space for neural architecture search and empirically produces good networks. | 1,440 | scitldr |
Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks. Despite its success as a defense mechanism, adversarial training fails to generalize well to unperturbed test set. We hypothesize that this poor generalization is a consequence of adversarial ... | Instance adaptive adversarial training for improving robustness-accuracy tradeoff | 1,441 | scitldr |
Machine learning models including traditional models and neural networks can be easily fooled by adversarial examples which are generated from the natural examples with small perturbations. This poses a critical challenge to machine learning security, and impedes the wide application of machine learning in many importa... | We propose a defensive distinction protection approach and demonstrate the strong distinguishability of adversarial examples. | 1,442 | scitldr |
For a long time, designing neural architectures that exhibit high performance was considered a dark art that required expert hand-tuning. One of the few well-known guidelines for architecture design is the avoidance of exploding or vanishing gradients. However, even this guideline has remained relatively vague and circ... | We introduce the NLC, a metric that is cheap to compute in the networks randomly initialized state and is highly predictive of generalization, at least in fully-connected networks. | 1,443 | scitldr |
This paper gives a rigorous analysis of trained Generalized Hamming Networks (GHN) proposed by and discloses an interesting finding about GHNs, i.e. stacked convolution layers in a GHN is equivalent to a single yet wide convolution layer. The revealed equivalence, on the theoretical side, can be regarded as a construct... | bridge the gap in soft computing | 1,444 | scitldr |
There are two major paradigms of white-box adversarial attacks that attempt to impose input perturbations. The first paradigm, called the fix-perturbation attack, crafts adversarial samples within a given perturbation level. The second paradigm, called the zero-confidence attack, finds the smallest perturbation needed ... | This paper introduces MarginAttack, a stronger and faster zero-confidence adversarial attack. | 1,445 | scitldr |
Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field. Rather than enumerate variations across filter channels or pyramid levels, dynamic models locally predict scale and adapt receptive fields accordingly. The degree... | Unsupervised optimization during inference gives top-down feedback to iteratively adjust feedforward prediction of scale variation for more equivariant recognition. | 1,446 | scitldr |
The Deep Image Prior is a fascinating recent approach for recovering images which appear natural, yet is not fully understood. This work aims at shedding some further light on this approach by investigating the properties of the early outputs of the DIP. First, we show that these early iterations demonstrate invariance... | We investigate properties of the recently introduced Deep Image Prior (Ulyanov et al, 2017) | 1,447 | scitldr |
In this paper, we address the challenge of limited labeled data and class imbalance problem for machine learning-based rumor detection on social media. We present an offline data augmentation method based on semantic relatedness for rumor detection. To this end, unlabeled social media data is exploited to augment limit... | We propose a methodology of augmenting publicly available data for rumor studies based on samantic relatedness between limited labeled and unlabeled data. | 1,448 | scitldr |
Self-attention is a useful mechanism to build generative models for language and images. It determines the importance of context elements by comparing each element to the current time step. In this paper, we show that a very lightweight convolution can perform competitively to the best reported self-attention . Next, w... | Dynamic lightweight convolutions are competitive to self-attention on language tasks. | 1,449 | scitldr |
Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond. By necessity, most theoretical guarantees revolve around convex-concave (or even linear) problems; however, making theoretical inroads towards effici... | We show how the inclusion of an extra-gradient step in first-order GAN training methods can improve stability and lead to improved convergence results. | 1,450 | scitldr |
Batch normalization (BN) is often used in an attempt to stabilize and accelerate training in deep neural networks. In many cases it indeed decreases the number of parameter updates required to achieve low training error. However, it also reduces robustness to small adversarial input perturbations and common corruptions... | Batch normalization reduces robustness at test-time to common corruptions and adversarial examples. | 1,451 | scitldr |
Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimization problems present interesting challenges for existing reinf... | We learn an optimization algorithm that generalizes to unseen tasks | 1,452 | scitldr |
The dependency of the generalization error of neural networks on model and dataset size is of critical importance both in practice and for understanding the theory of neural networks. Nevertheless, the functional form of this dependency remains elusive. In this work, we present a functional form which approximates well... | We predict the generalization error and specify the model which attains it across model/data scales. | 1,453 | scitldr |
Learning to control an environment without hand-crafted rewards or expert data remains challenging and is at the frontier of reinforcement learning research. We present an unsupervised learning algorithm to train agents to achieve perceptually-specified goals using only a stream of observations and actions. Our agent s... | Unsupervised reinforcement learning method for learning a policy to robustly achieve perceptually specified goals. | 1,454 | scitldr |
State of the art sequence-to-sequence models for large scale tasks perform a fixed number of computations for each input sequence regardless of whether it is easy or hard to process. In this paper, we train Transformer models which can make output predictions at different stages of the network and we investigate differ... | Sequence model that dynamically adjusts the amount of computation for each input. | 1,455 | scitldr |
Variational Auto-encoders (VAEs) are deep generative latent variable models consisting of two components: a generative model that captures a data distribution p(x) by transforming a distribution p(z) over latent space, and an inference model that infers likely latent codes for each data point . Recent work shows that t... | We characterize problematic global optima of the VAE objective and present a novel inference method to avoid such optima. | 1,456 | scitldr |
Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, such as WordNet, limit the coverage of these models, which can be addressed by learning hypernyms from un... | We propose a novel unsupervised word embedding which preserves the inclusion property in the context distribution and achieve state-of-the-art results on unsupervised hypernymy detection | 1,457 | scitldr |
Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the model has a fixed capacity. Current regularization-based continual learning algorithms need an external representation and extra computation ... | A regularization-based approach for continual learning using Bayesian neural networks to predict parameters' importance | 1,458 | scitldr |
Humans have a natural curiosity to imagine what it feels like to exist as someone or something else. This curiosity becomes even stronger for the pets we care for. Humans cannot truly know what it is like to be our pets, but we can deepen our understanding of what it is like to perceive and explore the world like them.... | This paper explores using wearable sensory augmenting technology to facilitate first-hand perspective-taking of what it is like to have cat-like whiskers. | 1,459 | scitldr |
Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data. Proving tight generalization error bounds is a central question in statistical learning theory. In this paper, we obtain generalization error bounds for learnin... | We give some generalization error bounds of noisy gradient methods such as SGLD, Langevin dynamics, noisy momentum and so forth. | 1,460 | scitldr |
In this paper, a new intrinsic reward generation method for sparse-reward reinforcement learning is proposed based on an ensemble of dynamics models. In the proposed method, the mixture of multiple dynamics models is used to approximate the true unknown transition probability, and the intrinsic reward is designed as th... | For sparse-reward reinforcement learning, the ensemble of multiple dynamics models is used to generate intrinsic reward designed as the minimum of the surprise. | 1,461 | scitldr |
Given the fast development of analysis techniques for NLP and speech processing systems, few systematic studies have been conducted to compare the strengths and weaknesses of each method. As a step in this direction we study the case of representations of phonology in neural network models of spoken language. We use tw... | We study representations of phonology in neural network models of spoken language with several variants of analytical techniques. | 1,462 | scitldr |
Super Resolution (SR) is a fundamental and important low-level computer vision (CV) task. Different from traditional SR models, this study concentrates on a specific but realistic SR issue: How can we obtain satisfied SR from compressed JPG (C-JPG) image, which widely exists on the Internet. In general, C-JPG can relea... | We solve the specific SR issue of low-quality JPG images by functional sub-models. | 1,463 | scitldr |
Keyword spotting—or wakeword detection—is an essential feature for hands-free operation of modern voice-controlled devices. With such devices becoming ubiquitous, users might want to choose a personalized custom wakeword. In this work, we present DONUT, a CTC-based algorithm for online query-by-example keyword spotting... | We propose an interpretable model for detecting user-chosen wakewords that learns from the user's examples. | 1,464 | scitldr |
To flexibly and efficiently reason about temporal sequences, abstract representations that compactly represent the important information in the sequence are needed. One way of constructing such representations is by focusing on the important events in a sequence. In this paper, we propose a model that learns both to di... | We propose a model that learns to discover informative frames in a future video sequence and represent the video via its keyframes. | 1,465 | scitldr |
This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representatio... | We learn deep representation by maximizing mutual information, leveraging structure in the objective, and are able to compute with fully supervised classifiers with comparable architectures | 1,466 | scitldr |
Estimating the importance of each atom in a molecule is one of the most appealing and challenging problems in chemistry, physics, and material engineering. The most common way to estimate the atomic importance is to compute the electronic structure using density-functional theory (DFT), and then to interpret it using d... | We first propose a fully-automated and target-directed atomic importance estimator based on the graph neural networks and a new concept of reverse self-attention. | 1,467 | scitldr |
Spectral clustering is a leading and popular technique in unsupervised data analysis. Two of its major limitations are scalability and generalization of the spectral embedding (i.e., out-of-sample-extension). In this paper we introduce a deep learning approach to spectral clustering that overcomes the above shortcoming... | Unsupervised spectral clustering using deep neural networks | 1,468 | scitldr |
Meta-learning methods, most notably Model-Agnostic Meta-Learning or MAML, have achieved great success in adapting to new tasks quickly, after having been trained on similar tasks. The mechanism behind their success, however, is poorly understood. We begin this work with an experimental analysis of MAML, finding that de... | We find that deep models are crucial for MAML to work and propose a method which enables effective meta-learning in smaller models. | 1,469 | scitldr |
Convolutional Neural Networks continuously advance the progress of 2D and 3D image and object classification. The steadfast usage of this algorithm requires constant evaluation and upgrading of foundational concepts to maintain progress. Network regularization techniques typically focus on convolutional layer operation... | Pooling is achieved using wavelets instead of traditional neighborhood approaches (max, average, etc). | 1,470 | scitldr |
Dynamic ridesharing services (DRS) play a major role in improving the efficiency of urban transportation. User satisfaction in dynamic ridesharing is determined by multiple factors such as travel time, cost, and social compatibility with co-passengers. Existing DRS optimize profit by maximizing the operational value fo... | We propose a novel dynamic ridesharing framework to form trips that optimizes both operational value for the service provider and user value to the passengers by factoring the users' social preferences into the decision-making process. | 1,471 | scitldr |
Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction. To better understand such attacks, a characterization is needed of the properties of regions (the so-called `adversarial subsp... | We characterize the dimensional properties of adversarial subspaces in the neighborhood of adversarial examples via the use of Local Intrinsic Dimensionality (LID). | 1,472 | scitldr |
In this paper, we design and analyze a new zeroth-order (ZO) stochastic optimization algorithm, ZO-signSGD, which enjoys dual advantages of gradient-free operations and signSGD. The latter requires only the sign information of gradient estimates but is able to achieve a comparable or even better convergence speed than ... | We design and analyze a new zeroth-order stochastic optimization algorithm, ZO-signSGD, and demonstrate its connection and application to black-box adversarial attacks in robust deep learning | 1,473 | scitldr |
The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This in increased uncertainties in the grid operation, thereby negatively affecting the reliability and ing in increased cost of operation. This research paper proposes ... | This paper proposes a Unified Recurrent Neural Network Architecture for short-term multi-time-horizon solar forecasting and validates the forecast performance gains over the previously reported methods | 1,474 | scitldr |
The ResNet and the batch-normalization (BN) achieved high performance even when only a few labeled data are available. However, the reasons for its high performance are unclear. To clear the reasons, we analyzed the effect of the skip-connection in ResNet and the BN on the data separation ability, which is an important... | The Skip-connection in ResNet and the batch-normalization improve the data separation ability and help to train a deep neural network. | 1,475 | scitldr |
Learning representations of data is an important issue in machine learning. Though GAN has led to significant improvements in the data representations, it still has several problems such as unstable training, hidden manifold of data, and huge computational overhead. GAN tends to produce the data simply without any info... | We propose a generative model that not only produces data with desired features from the pre-defined latent space but also fully understands the features of the data to create characteristics that are not in the dataset. | 1,476 | scitldr |
With the ever increasing demand and the ant reduced quality of services, the focus has shifted towards easing network congestion to enable more efficient flow in systems like traffic, supply chains and electrical grids. A step in this direction is to re-imagine the traditional heuristics based training of systems as th... | A framework for studying emergent communication in a networked multi-agent reinforcement learning setup. | 1,477 | scitldr |
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data. Translation equivariance is an important inductive bias for many learning problems including time series modelling, spatial data, and images. The model embeds ... | We extend deep sets to functional embeddings and Neural Processes to include translation equivariant members | 1,478 | scitldr |
Classical models describe primary visual cortex (V1) as a filter bank of orientation-selective linear-nonlinear (LN) or energy models, but these models fail to predict neural responses to natural stimuli accurately. Recent work shows that convolutional neural networks (CNNs) can be trained to predict V1 activity more a... | A rotation-equivariant CNN model of V1 that outperforms previous models and suggest functional groupings of V1 neurons. | 1,479 | scitldr |
In classic papers, Zellner demonstrated that Bayesian inference could be derived as the solution to an information theoretic functional. Below we derive a generalized form of this functional as a variational lower bound of a predictive information bottleneck objective. This generalized functional encompasses most moder... | Rederive a wide class of inference procedures from an global information bottleneck objective. | 1,480 | scitldr |
In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision about which input features are relevant. The information bottleneck method formalizes this as an information-theoretic optimization problem by maintaining an optimal tradeoff between ... | Training agents with adaptive computation based on information bottleneck can promote generalization. | 1,481 | scitldr |
Breathing exercises are an accessible way to manage stress and many mental illness symptoms. Traditionally, learning breathing exercises involved in-person guidance or audio recordings. The shift to mobile devices has led to a new way of learning and engaging in breathing exercises as seen in the rise of multiple mobil... | We utilized a within-subjects study to evaluate four paced breathing visuals common in mobile apps to understand which is most effective in providing breathing exercise guidance. | 1,482 | scitldr |
Current work on neural code synthesis consists of increasingly sophisticated architectures being trained on highly simplified domain-specific languages, using uniform sampling across program space of those languages for training. By comparison, program space for a C-like language is vast, and extremely sparsely populat... | A way to generate training corpora for neural code synthesis using a discriminator trained on unlabelled data | 1,483 | scitldr |
Neural reading comprehension models have recently achieved impressive gener- alisation , yet still perform poorly when given adversarially selected input. Most prior work has studied semantically invariant text perturbations which cause a model’s prediction to change when it should not. In this work we focus on the com... | We demonstrate vulnerability to undersensitivity attacks in SQuAD2.0 and NewsQA neural reading comprehension models, where the model predicts the same answer with increased confidence to adversarially chosen questions, and compare defence strategies. | 1,484 | scitldr |
This paper puts forward a new text to tensor representation that relies on information compression techniques to assign shorter codes to the most frequently used characters. This representation is language-independent with no need of pretraining and produces an encoding with no information loss. It provides an adequate... | Using Compressing tecniques to Encoding of Words is a possibility for faster training of CNN and dimensionality reduction of representation | 1,485 | scitldr |
Sequence prediction models can be learned from example sequences with a variety of training algorithms. Maximum likelihood learning is simple and efficient, yet can suffer from compounding error at test time. Reinforcement learning such as policy gradient addresses the issue but can have prohibitively poor exploration ... | An entropy regularized policy optimization formalism subsumes a set of sequence prediction learning algorithms. A new interpolation algorithm with improved results on text generation and game imitation learning. | 1,486 | scitldr |
Origin-Destination (OD) flow data is an important instrument in transportation studies. Precise prediction of customer demands from each original location to a destination given a series of previous snapshots helps ride-sharing platforms to better understand their market mechanism. However, most existing prediction met... | We propose a purely convolutional CNN model with attention mechanism to predict spatial-temporal origin-destination flows. | 1,487 | scitldr |
Adversarial training has been demonstrated as one of the most effective methods for training robust models to defend against adversarial examples. However, adversarially trained models often lack adversarially robust generalization on unseen testing data. Recent works show that adversarially trained models are more bia... | We propose a new stream of adversarial training approach called Robust Local Features for Adversarial Training (RLFAT) that significantly improves both the adversarially robust generalization and the standard generalization. | 1,488 | scitldr |
The verification of planning domain models is crucial to ensure the safety, integrity and correctness of planning-based automated systems. This task is usually performed using model checking techniques. However, directly applying model checkers to verify planning domain models can in false positives, i.e. counterexampl... | Why and how to constrain planning domain model verification with planning goals to avoid unreachable counterexamples (false positives verification outcomes). | 1,489 | scitldr |
We've seen tremendous success of image generating models these years. Generating images through a neural network is usually pixel-based, which is fundamentally different from how humans create artwork using brushes. To imitate human drawing, interactions between the environment and the agent is required to allow trials... | StrokeNet is a novel architecture where the agent is trained to draw by strokes on a differentiable simulation of the environment, which could effectively exploit the power of back-propagation. | 1,490 | scitldr |
We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangle... | We demonstrate how machine learning is able to model experiments in quantum physics. | 1,491 | scitldr |
In this paper, we propose a nonlinear unsupervised metric learning framework to boost of the performance of clustering algorithms. Under our framework, nonlinear distance metric learning and manifold embedding are integrated and conducted simultaneously to increase the natural separations among data samples. The metric... | a nonlinear unsupervised metric learning framework to boost the performance of clustering algorithms. | 1,492 | scitldr |
As machine learning becomes ubiquitous, deployed systems need to be as accu- rate as they can. As a , machine learning service providers have a surging need for useful, additional training data that benefits training, without giving up all the details about the trained program. At the same time, data owners would like ... | Facing complex, black-box models, encrypting the data is not as usable as approximating the model and using it to price a potential transaction. | 1,493 | scitldr |
We present Line-Storm, an interactive computer system for creative performance. The context we investigated was writing on paper using Line-Storm. We used self-report questionnaires as part of research involving human participants, to evaluate Line-Storm. Line-Storm consisted of a writing stylus and writing pad, augmen... | Interactive stylus based sound incorporating writing system | 1,494 | scitldr |
Language style transfer is the problem of migrating the content of a source sentence to a target style. In many applications, parallel training data are not available and source sentences to be transferred may have arbitrary and unknown styles. In this paper, we present an encoder-decoder framework under this problem s... | We present an encoder-decoder framework for language style transfer, which allows for the use of non-parallel data and source data with various unknown language styles. | 1,495 | scitldr |
In this paper, we propose a new loss function for performing principal component analysis (PCA) using linear autoencoders (LAEs). Optimizing the standard L2 loss in a decoder matrix that spans the principal subspace of the sample covariance of the data, but fails to identify the exact eigenvectors. This downside origin... | A new loss function for PCA with linear autoencoders that provably yields ordered exact eigenvectors | 1,496 | scitldr |
Users have tremendous potential to aid in the construction and maintenance of knowledges bases (KBs) through the contribution of feedback that identifies incorrect and missing entity attributes and relations. However, as new data is added to the KB, the KB entities, which are constructed by running entity resolution (E... | This paper develops a framework for integrating user feedback under identity uncertainty in knowledge bases. | 1,497 | scitldr |
Machine learning algorithms are vulnerable to poisoning attacks: An adversary can inject malicious points in the training dataset to influence the learning process and degrade the algorithm's performance. Optimal poisoning attacks have already been proposed to evaluate worst-case scenarios, modelling attacks as a bi-le... | In this paper we propose a novel generative model to craft systematic poisoning attacks with detectability constraints against machine learning classifiers, including deep networks. | 1,498 | scitldr |
The Expectation-Maximization (EM) algorithm is a fundamental tool in unsupervised machine learning. It is often used as an efficient way to solve Maximum Likelihood (ML) and Maximum A Posteriori estimation problems, especially for models with latent variables. It is also the algorithm of choice to fit mixture models: g... | It's the quantum algorithm for Expectation Maximization. It's fast: the runtime depends only polylogarithmically on the number of elements in the dataset. | 1,499 | scitldr |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.