query stringlengths 273 149k | pos stringlengths 18 667 | idx int64 0 1.99k | task_name stringclasses 1
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Emoji suggestion systems based on typed text have been proposed to encourage emoji usage and enrich text messaging; however, such systems’ actual effects on the chat experience remain unknown. We built an Android keyboard with both lexical (word-based) and semantic (meaning-based) emoji suggestion capabilities and comp... | We built an Android keyboard with both lexical (word-based) and semantic (meaning-based) emoji suggestion capabilities and compared their effects in two different chat studies. | 1,300 | scitldr |
Conventional Generative Adversarial Networks (GANs) for text generation tend to have issues of reward sparsity and mode collapse that affect the quality and diversity of generated samples. To address the issues, we propose a novel self-adversarial learning (SAL) paradigm for improving GANs' performance in text generati... | We propose a self-adversarial learning (SAL) paradigm which improves the generator in a self-play fashion for improving GANs' performance in text generation. | 1,301 | scitldr |
Determining the number of latent dimensions is a ubiquitous problem in machine learning. In this study, we introduce a novel method that relies on SVD to discover the number of latent dimensions. The general principle behind the method is to compare the curve of singular values of the SVD decomposition of a data set wi... | In this study, we introduce a novel method that relies on SVD to discover the number of latent dimensions. | 1,302 | scitldr |
Deep learning models are often sensitive to adversarial attacks, where carefully-designed input samples can cause the system to produce incorrect decisions. Here we focus on the problem of detecting attacks, rather than robust classification, since detecting that an attack occurs may be even more important than avoidin... | A novel adversarial detection approach, which uses explainability methods to identify images whose explanations are inconsistent with the predicted class. | 1,303 | scitldr |
We describe a simple and general neural network weight compression approach, in which the network parameters (weights and biases) are represented in a “latent” space, amounting to a reparameterization. This space is equipped with a learned probability model, which is used to impose an entropy penalty on the parameter r... | An end-to-end trainable model compression method optimizing accuracy jointly with the expected model size. | 1,304 | scitldr |
Neural networks can converge faster with help from a smarter batch selection strategy. In this regard, we propose Ada-Boundary, a novel adaptive-batch selection algorithm that constructs an effective mini-batch according to the learning progress of the model. Our key idea is to present confusing samples what the true l... | We suggest a smart batch selection technique called Ada-Boundary. | 1,305 | scitldr |
State of the art sound event classification relies in neural networks to learn the associations between class labels and audio recordings within a dataset. These datasets typically define an ontology to create a structure that relates these sound classes with more abstract super classes. Hence, the ontology serves as a... | We present ontology-based neural network architectures for sound event classification. | 1,306 | scitldr |
In contrast to fully connected networks, Convolutional Neural Networks (CNNs) achieve efficiency by learning weights associated with local filters with a finite spatial extent. An implication of this is that a filter may know what it is looking at, but not where it is positioned in the image. Information concerning abs... | Our work shows positional information has been implicitly encoded in a network. This information is important for detecting position-dependent features, e.g. semantic and saliency. | 1,307 | scitldr |
Semantic parsing which maps a natural language sentence into a formal machine-readable representation of its meaning, is highly constrained by the limited annotated training data. Inspired by the idea of coarse-to-fine, we propose a general-to-detailed neural network(GDNN) by incorporating cross-domain sketch(CDS) amon... | General-to-detailed neural network(GDNN) with Multi-Task Learning by incorporating cross-domain sketch(CDS) for semantic parsing | 1,308 | scitldr |
The learnability of different neural architectures can be characterized directly by computable measures of data complexity. In this paper, we reframe the problem of architecture selection as understanding how data determines the most expressive and generalizable architectures suited to that data, beyond inductive bias.... | We show that the learnability of different neural architectures can be characterized directly by computable measures of data complexity. | 1,309 | scitldr |
Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space. We present a... | Tackling inverse design via genetic algorithms augmented with deep neural networks. | 1,310 | scitldr |
Bidirectional Encoder Representations from Transformers (BERT) reach state-of-the-art in a variety of Natural Language Processing tasks. However, understanding of their internal functioning is still insufficient and unsatisfactory. In order to better understand BERT and other Transformer-based models, we present a laye... | We investigate hidden state activations of Transformer Models in Question Answering Tasks. | 1,311 | scitldr |
We propose a general deep reinforcement learning method and apply it to robot manipulation tasks. Our approach leverages demonstration data to assist a reinforcement learning agent in learning to solve a wide range of tasks, mainly previously unsolved. We train visuomotor policies end-to-end to learn a direct mapping f... | combine reinforcement learning and imitation learning to solve complex robot manipulation tasks from pixels | 1,312 | scitldr |
Ensembles, where multiple neural networks are trained individually and their predictions are averaged, have been shown to be widely successful for improving both the accuracy and predictive uncertainty of single neural networks. However, an ensemble's cost for both training and testing increases linearly with the numbe... | We introduced BatchEnsemble, an efficient method for ensembling and lifelong learning which can be used to improve the accuracy and uncertainty of any neural network like typical ensemble methods. | 1,313 | scitldr |
Reinforcement learning typically requires carefully designed reward functions in order to learn the desired behavior. We present a novel reward estimation method that is based on a finite sample of optimal state trajectories from expert demon- strations and can be used for guiding an agent to mimic the expert behavior.... | Reward Estimation from Game Videos | 1,314 | scitldr |
Deep neural networks have achieved impressive performance in handling complicated semantics in natural language, while mostly treated as black boxes. To explain how the model handles compositional semantics of words and phrases, we study the hierarchical explanation problem. We highlight the key challenge is to compute... | We propose measurement of phrase importance and algorithms for hierarchical explanation of neural sequence model predictions | 1,315 | scitldr |
Stochastic gradient descent (SGD) with stochastic momentum is popular in nonconvex stochastic optimization and particularly for the training of deep neural networks. In standard SGD, parameters are updated by improving along the path of the gradient at the current iterate on a batch of examples, where the addition of a... | Higher momentum parameter $\beta$ helps for escaping saddle points faster | 1,316 | scitldr |
GANs provide a framework for training generative models which mimic a data distribution. However, in many cases we wish to train a generative model to optimize some auxiliary objective function within the data it generates, such as making more aesthetically pleasing images. In some cases, these objective functions are ... | We describe how to improve an image generative model according to a slow- or difficult-to-evaluate objective, such as human feedback, which could have many applications, like making more aesthetic images. | 1,317 | scitldr |
Semi-supervised learning (SSL) is a study that efficiently exploits a large amount of unlabeled data to improve performance in conditions of limited labeled data. Most of the conventional SSL methods assume that the classes of unlabeled data are included in the set of classes of labeled data. In addition, these methods... | Our proposed algorithm does not use all of the unlabeled data for the training, and it rather uses them selectively. | 1,318 | scitldr |
Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition generation without retrai... | A new approach to conditional generation by constraining the latent space of an unconditional generative model. | 1,319 | scitldr |
Recent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in accuracy across many NLP tasks. However, these accuracy improvements depend on the availability of exceptionally large computat... | We quantify the energy cost in terms of money (cloud credits) and carbon footprint of training recently successful neural network models for NLP. Costs are high. | 1,320 | scitldr |
Many models based on the Variational Autoencoder are proposed to achieve disentangled latent variables in inference. However, most current work is focusing on designing powerful disentangling regularizers, while the given number of dimensions for the latent representation at initialization could severely influence the d... | The Pruning VAE is proposed to search for disentangled variables with intrinsic dimension. | 1,321 | scitldr |
We explore the properties of byte-level recurrent language models. When given sufficient amounts of capacity, training data, and compute time, the representations learned by these models include disentangled features corresponding to high-level concepts. Specifically, we find a single unit which performs sentiment anal... | Byte-level recurrent language models learn high-quality domain specific representations of text. | 1,322 | scitldr |
Discrete latent-variable models, while applicable in a variety of settings, can often be difficult to learn. Sampling discrete latent variables can in high-variance gradient estimators for two primary reasons: 1) branching on the samples within the model, and 2) the lack of a pathwise derivative for the samples. While ... | Empirical analysis and explanation of particle-based gradient estimators for approximate inference with deep generative models. | 1,323 | scitldr |
The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set. Unfortunately, state-of-the-art deep extreme classifiers are either not scalable or inaccurate for sh... | Scalable and accurate deep multi label learning with millions of labels. | 1,324 | scitldr |
Robust estimation under Huber's $\epsilon$-contamination model has become an important topic in statistics and theoretical computer science. Rate-optimal procedures such as Tukey's median and other estimators based on statistical depth functions are impractical because of their computational intractability. In this pap... | GANs are shown to provide us a new effective robust mean estimate against agnostic contaminations with both statistical optimality and practical tractability. | 1,325 | scitldr |
Long Short-Term Memory (LSTM) is one of the most powerful sequence models. Despite the strong performance, however, it lacks the nice interpretability as in state space models. In this paper, we present a way to combine the best of both worlds by introducing State Space LSTM (SSL), which generalizes the earlier work \c... | We present State Space LSTM models, a combination of state space models and LSTMs, and propose an inference algorithm based on sequential Monte Carlo. | 1,326 | scitldr |
Given a large database of concepts but only one or a few examples of each, can we learn models for each concept that are not only generalisable, but interpretable? In this work, we aim to tackle this problem through hierarchical Bayesian program induction. We present a novel learning algorithm which can infer concepts ... | We extend the wake-sleep algorithm and use it to learn to learn structured models from few examples, | 1,327 | scitldr |
The knowledge regarding the function of proteins is necessary as it gives a clear picture of biological processes. Nevertheless, there are many protein sequences found and added to the databases but lacks functional annotation. The laboratory experiments take a considerable amount of time for annotation of the sequence... | Proteins, amino-acid sequences, machine learning, deep learning, recurrent neural network(RNN), long short term memory(LSTM), gated recurrent unit(GRU), deep neural networks | 1,328 | scitldr |
Spoken term detection (STD) is the task of determining whether and where a given word or phrase appears in a given segment of speech. Algorithms for STD are often aimed at maximizing the gap between the scores of positive and negative examples. As such they are focused on ensuring that utterances where the term appears... | Spoken Term Detection, using structured prediction and deep networks, implementing a new loss function that both maximizes AUC and ranks according to a predefined threshold. | 1,329 | scitldr |
Federated learning involves jointly learning over massively distributed partitions of data generated on remote devices. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we propose q-Fair Federated Learning (q-FFL), a nove... | We propose a novel optimization objective that encourages fairness in heterogeneous federated networks, and develop a scalable method to solve it. | 1,330 | scitldr |
We propose a novel autoencoding model called Pairwise Augmented GANs. We train a generator and an encoder jointly and in an adversarial manner. The generator network learns to sample realistic objects. In turn, the encoder network at the same time is trained to map the true data distribution to the prior in latent spac... | We propose a novel autoencoding model with augmented adversarial reconstruction loss. We intoduce new metric for content-based assessment of reconstructions. | 1,331 | scitldr |
We propose a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables. Inspired by dropout, a popular tool for regularization and model ensemble, we assign sparse priors to the weights in deep neural networks (DNN) in order to achieve automatic “dropout” and avoid over-fitting. By alter... | a robust Bayesian deep learning algorithm to infer complex posteriors with latent variables | 1,332 | scitldr |
Activity of populations of sensory neurons carries stimulus information in both the temporal and the spatial dimensions. This poses the question of how to compactly represent all the information that the population codes carry across all these dimensions. Here, we developed an analytical method to factorize a large num... | We extended single-trial space-by-time tensor decomposition based on non-negative matrix factorization to efficiently discount pre-stimulus baseline activity that improves decoding performance on data with non-negligible baselines. | 1,333 | scitldr |
We propose a framework for extreme learned image compression based on Generative Adversarial Networks (GANs), obtaining visually pleasing images at significantly lower bitrates than previous methods. This is made possible through our GAN formulation of learned compression combined with a generator/decoder which operate... | GAN-based extreme image compression method using less than half the bits of the SOTA engineered codec while preserving visual quality | 1,334 | scitldr |
In learning to rank, one is interested in optimising the global ordering of a list of items according to their utility for users. Popular approaches learn a scoring function that scores items individually (i.e. without the context of other items in the list) by optimising a pointwise, pairwise or listwise loss. The lis... | Learning to rank using the Transformer architecture. | 1,335 | scitldr |
We propose an active learning algorithmic architecture, capable of organizing its learning process in order to achieve a field of complex tasks by learning sequences of primitive motor policies: Socially Guided Intrinsic Motivation with Procedure Babbling (SGIM-PB). The learner can generalize over its experience to con... | The paper describes a strategic intrinsically motivated learning algorithm which tackles the learning of complex motor policies. | 1,336 | scitldr |
Monte Carlo Tree Search (MCTS) has achieved impressive on a range of discrete environments, such as Go, Mario and Arcade games, but it has not yet fulfilled its true potential in continuous domains. In this work, we introduceTPO, a tree search based policy optimization method for continuous environments. TPO takes a hy... | We use MCTS to further optimize a bootstrapped policy for continuous action spaces under a policy iteration setting. | 1,337 | scitldr |
The variational autoencoder (VAE) has found success in modelling the manifold of natural images on certain datasets, allowing meaningful images to be generated while interpolating or extrapolating in the latent code space, but it is unclear whether similar capabilities are feasible for text considering its discrete nat... | why previous VAEs on text cannot learn controllable latent representation as on images, as well as a fix to enable the first success towards controlled text generation without supervision | 1,338 | scitldr |
In this paper we developed a hierarchical network model, called Hierarchical Prediction Network (HPNet) to understand how spatiotemporal memories might be learned and encoded in a representational hierarchy for predicting future video frames. The model is inspired by the feedforward, feedback and lateral recurrent circ... | A new hierarchical cortical model for encoding spatiotemporal memory and video prediction | 1,339 | scitldr |
Saliency maps are often used to suggest explanations of the behavior of deep rein- forcement learning (RL) agents. However, the explanations derived from saliency maps are often unfalsifiable and can be highly subjective. We introduce an empirical approach grounded in counterfactual reasoning to test the hypotheses gen... | Proposing a new counterfactual-based methodology to evaluate the hypotheses generated from saliency maps about deep RL agent behavior. | 1,340 | scitldr |
One of the unresolved questions in deep learning is the nature of the solutions that are being discovered. We investigate the collection of solutions reached by the same network architecture, with different random initialization of weights and random mini-batches. These solutions are shown to be rather similar - more o... | Most neural networks approximate the same classification function, even across architectures, through all stages of learning. | 1,341 | scitldr |
We propose a unified framework for building unsupervised representations of individual objects or entities (and their compositions), by associating with each object both a distributional as well as a point estimate (vector embedding). This is made possible by the use of optimal transport, which allows us to build these... | Represent each entity based on its histogram of contexts and then Wasserstein is all you need! | 1,342 | scitldr |
In this paper, we propose two methods, namely Trace-norm regression (TNR) and Stable Trace-norm Analysis (StaTNA), to improve performances of recommender systems with side information. Our trace-norm regression approach extracts low-rank latent factors underlying the side information that drives user preference under d... | Methodologies for recommender systems with side information based on trace-norm regularization | 1,343 | scitldr |
This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. Text-based computer games describe their world to the player through natural language and expect the player to interact with the game using text. These games are of interest... | This work presents an exploration and imitation-learning-based agent capable of state-of-the-art performance in playing text-based computer games. | 1,344 | scitldr |
The recent “Lottery Ticket Hypothesis” paper by Frankle & Carbin showed that a simple approach to creating sparse networks (keep the large weights) in models that are trainable from scratch, but only when starting from the same initial weights. The performance of these networks often exceeds the performance of the non-... | In neural network pruning, zeroing pruned weights is important, sign of initialization is key, and masking can be thought of as training. | 1,345 | scitldr |
Fine-tuning with pre-trained models has achieved exceptional for many language tasks. In this study, we focused on one such self-attention network model, namely BERT, which has performed well in terms of stacking layers across diverse language-understanding benchmarks. However, in many downstream tasks, information bet... | We proposed SesameBERT, a generalized fine-tuning method that enables the extraction of global information among all layers through Squeeze and Excitation and enriches local information by capturing neighboring contexts via Gaussian blurring. | 1,346 | scitldr |
A growing number of learning methods are actually differentiable games whose players optimise multiple, interdependent objectives in parallel – from GANs and intrinsic curiosity to multi-agent RL. Opponent shaping is a powerful approach to improve learning dynamics in these games, accounting for player influence on oth... | Opponent shaping is a powerful approach to multi-agent learning but can prevent convergence; our SOS algorithm fixes this with strong guarantees in all differentiable games. | 1,347 | scitldr |
The rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them, leaving many questions unanswered or inadequately answered. Many of these questions are not unique, and reliable identification of similar questions would enable more efficient and effective quest... | We show that question-answer matching is a particularly good pre-training task for question-similarity and release a dataset for medical question similarity | 1,348 | scitldr |
We study the benefit of sharing representations among tasks to enable the effective use of deep neural networks in Multi-Task Reinforcement Learning. We leverage the assumption that learning from different tasks, sharing common properties, is helpful to generalize the knowledge of them ing in a more effective feature e... | A study on the benefit of sharing representation in Multi-Task Reinforcement Learning. | 1,349 | scitldr |
We present a 3D capsule architecture for processing of point clouds that is equivariant with respect to the SO rotation group, translation and permutation of the unordered input sets. The network operates on a sparse set of local reference frames, computed from an input point cloud and establishes end-to-end equivarian... | Deep architectures for 3D point clouds that are equivariant to SO(3) rotations, as well as translations and permutations. | 1,350 | scitldr |
Vector semantics, especially sentence vectors, have recently been used successfully in many areas of natural language processing. However, relatively little work has explored the internal structure and properties of spaces of sentence vectors. In this paper, we will explore the properties of sentence vectors by studyin... | A comparison and detailed analysis of various sentence embedding models through the real-world task of automatic summarization. | 1,351 | scitldr |
We present Value Propagation (VProp), a parameter-efficient differentiable planning module built on Value Iteration which can successfully be trained in a reinforcement learning fashion to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments. We eva... | We propose Value Propagation, a novel end-to-end planner which can learn to solve 2D navigation tasks via Reinforcement Learning, and that generalizes to larger and dynamic environments. | 1,352 | scitldr |
Recommendation is a prevalent application of machine learning that affects many users; therefore, it is crucial for recommender models to be accurate and interpretable. In this work, we propose a method to both interpret and augment the predictions of black-box recommender systems. In particular, we propose to extract ... | Proposed a method to extract and leverage interpretations of feature interactions | 1,353 | scitldr |
Rectified linear units, or ReLUs, have become a preferred activation function for artificial neural networks. In this paper we consider the problem of learning a generative model in the presence of nonlinearity (modeled by the ReLU functions). Given a set of signal vectors $\mathbf{y}^i \in \mathbb{R}^d, i =1, 2, \dots... | We show that it is possible to recover the parameters of a 1-layer ReLU generative model from looking at samples generated by it | 1,354 | scitldr |
Methods that calculate dense vector representations for features in unstructured data—such as words in a document—have proven to be very successful for knowledge representation. We study how to estimate dense representations when multiple feature types exist within a dataset for supervised learning where explicit label... | Learn dense vector representations of arbitrary types of features in labeled and unlabeled datasets | 1,355 | scitldr |
We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language. Specifically, we train a RNN on positive and negative examples from a regular language, and ask if there is a simple decoding function that maps states of this RNN to states of t... | Finite Automata Can be Linearly decoded from Language-Recognizing RNNs using low coarseness abstraction functions and high accuracy decoders. | 1,356 | scitldr |
Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, ... | We propose HURRICANE to address the challenge of hardware diversity in one-shot neural architecture search | 1,357 | scitldr |
In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our method explicitly models the phrase structures in output sequences using Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence modeling method. To mitigate the monotonic alignment requirement of SWAN, we introduce a new ... | Neural phrase-based machine translation with linear decoding time | 1,358 | scitldr |
Generative Adversarial Networks (GANs) have shown impressive in modeling distributions over complicated manifolds such as those of natural images. However, GANs often suffer from mode collapse, which means they are prone to characterize only a single or a few modes of the data distribution. In order to address this pro... | We propose an AE-based GAN that alleviates mode collapse in GANs. | 1,359 | scitldr |
Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional compilation heuristics, or very recently genetic algorithms and other stochastic meth... | Reinforcement learning and Adaptive Sampling for Optimized Compilation of Deep Neural Networks. | 1,360 | scitldr |
In this paper, we propose a differentiable adversarial grammar model for future prediction. The objective is to model a formal grammar in terms of differentiable functions and latent representations, so that their learning is possible through standard backpropagation. Learning a formal grammar represented with latent t... | We design a grammar that is learned in an adversarial setting and apply it to future prediction in video. | 1,361 | scitldr |
We consider a simple and overarching representation for permutation-invariant functions of sequences (or set functions). Our approach, which we call Janossy pooling, expresses a permutation-invariant function as the average of a permutation-sensitive function applied to all reorderings of the input sequence. This allow... | We propose Janossy pooling, a method for learning deep permutation invariant functions designed to exploit relationships within the input sequence and tractable inference strategies such as a stochastic optimization procedure we call piSGD | 1,362 | scitldr |
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that number of instances per task and class is fixed. Due to such restriction, they learn to equally utilize the meta-knowledge across all the tasks, ev... | A novel meta-learning model that adaptively balances the effect of the meta-learning and task-specific learning, and also class-specific learning within each task. | 1,363 | scitldr |
Many tasks in artificial intelligence require the collaboration of multiple agents. We exam deep reinforcement learning for multi-agent domains. Recent research efforts often take the form of two seemingly conflicting perspectives, the decentralized perspective, where each agent is supposed to have its own controller; ... | We revisit the idea of the master-slave architecture in multi-agent deep reinforcement learning and outperforms state-of-the-arts. | 1,364 | scitldr |
We study the implicit bias of gradient descent methods in solving a binary classification problem over a linearly separable dataset. The classifier is described by a nonlinear ReLU model and the objective function adopts the exponential loss function. We first characterize the landscape of the loss function and show th... | We study the implicit bias of gradient methods in solving a binary classification problem with nonlinear ReLU models. | 1,365 | scitldr |
Model pruning has become a useful technique that improves the computational efficiency of deep learning, making it possible to deploy solutions in resource-limited scenarios. A widely-used practice in relevant work assumes that a smaller-norm parameter or feature plays a less informative role at the inference time. In ... | A CNN model pruning method using ISTA and rescaling trick to enforce sparsity of scaling parameters in batch normalization. | 1,366 | scitldr |
Stochastic gradient descent (SGD) has been the dominant optimization method for training deep neural networks due to its many desirable properties. One of the more remarkable and least understood quality of SGD is that it generalizes relatively well on unseen data even when the neural network has millions of parameters... | What can we learn about training neural networks if we treat each layer as a gradient boosting problem? | 1,367 | scitldr |
We propose to extend existing deep reinforcement learning (Deep RL) algorithms by allowing them to additionally choose sequences of actions as a part of their policy. This modification forces the network to anticipate the reward of action sequences, which, as we show, improves the exploration leading to better converge... | Anticipation improves convergence of deep reinforcement learning. | 1,368 | scitldr |
To optimize a neural network one often thinks of optimizing its parameters, but it is ultimately a matter of optimizing the function that maps inputs to outputs. Since a change in the parameters might serve as a poor proxy for the change in the function, it is of some concern that primacy is given to parameters but tha... | We find movement in function space is not proportional to movement in parameter space during optimization. We propose a new natural-gradient style optimizer to address this. | 1,369 | scitldr |
The information bottleneck (IB) problem tackles the issue of obtaining relevant compressed representations T of some random variable X for the task of predicting Y. It is defined as a constrained optimization problem which maximizes the information the representation has about the task, I(T;Y), while ensuring that a mi... | We introduce a general family of Lagrangians that allow exploring the IB curve in all scenarios. When these are used, and the IB curve is known, one can optimize directly for a performance/compression level directly. | 1,370 | scitldr |
We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. NLMs exploit the power of both neural networks---as function approximators, and logic programming---as a symbolic processor for objects with properties, relations, logic connectives, and quantifier... | We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning. | 1,371 | scitldr |
Sequence-to-sequence (seq2seq) neural models have been actively investigated for abstractive summarization. Nevertheless, existing neural abstractive systems frequently generate factually incorrect summaries and are vulnerable to adversarial information, suggesting a crucial lack of semantic understanding. In this pape... | We propose a semantic-aware neural abstractive summarization model and a novel automatic summarization evaluation scheme that measures how well a model identifies off-topic information from adversarial samples. | 1,372 | scitldr |
The recent work of Super Characters method using two-dimensional word embedding achieved state-of-the-art in text classification tasks, showcasing the promise of this new approach. This paper borrows the idea of Super Characters method and two-dimensional embedding, and proposes a method of generating conversational re... | Print the input sentence and current response sentence onto an image and use fine-tuned ImageNet CNN model to predict the next response word. | 1,373 | scitldr |
Convolutional neural networks (CNNs) have been generally acknowledged as one of the driving forces for the advancement of computer vision. Despite their promising performances on many tasks, CNNs still face major obstacles on the road to achieving ideal machine intelligence. One is that CNNs are complex and hard to int... | We enable ordinary CNNs for few-shot learning by exploiting visual concepts which are interpretable visual cues learnt within CNNs. | 1,374 | scitldr |
Recently various neural networks have been proposed for irregularly structured data such as graphs and manifolds. To our knowledge, all existing graph networks have discrete depth. Inspired by neural ordinary differential equation (NODE) for data in the Euclidean domain, we extend the idea of continuous-depth models to... | Apply ordinary differential equation model on graph structured data | 1,375 | scitldr |
Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time. In this paper, we formulate a new task of unsupervised video-to-video translation, which poses its own unique challenges. Translating video impl... | Proposed new task, datasets and baselines; 3D Conv CycleGAN preserves object properties across frames; batch structure in frame-level methods matters. | 1,376 | scitldr |
Capsule networks are constrained by the parameter-expensive nature of their layers, and the general lack of provable equivariance guarantees. We present a variation of capsule networks that aims to remedy this. We identify that learning all pair-wise part-whole relationships between capsules of successive layers is ine... | A new scalable, group-equivariant model for capsule networks that preserves compositionality under transformations, and is empirically more transformation-robust to older capsule network models. | 1,377 | scitldr |
Recently deep neural networks have shown their capacity to memorize training data, even with noisy labels, which hurts generalization performance. To mitigate this issue, we propose a simple but effective method that is robust to noisy labels, even with severe noise. Our objective involves a variance regularization ter... | The paper proposed a simple yet effective baseline for learning with noisy labels. | 1,378 | scitldr |
Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese--English news translation task. We empirically test this claim with alternative evaluation protocols, contrasting the evaluation of single sentences and entire documents. In a pairwise ranking... | Raters prefer adequacy in human over machine translation when evaluating entire documents, but not when evaluating single sentences. | 1,379 | scitldr |
Imitation learning aims to inversely learn a policy from expert demonstrations, which has been extensively studied in the literature for both single-agent setting with Markov decision process (MDP) model, and multi-agent setting with Markov game (MG) model. However, existing approaches for general multi-agent Markov ga... | This paper extends the multi-agent generative adversarial imitation learning to extensive-form Markov games. | 1,380 | scitldr |
Self-training is one of the earliest and simplest semi-supervised methods. The key idea is to augment the original labeled dataset with unlabeled data paired with the model’s prediction. Self-training has mostly been well-studied to classification problems. However, in complex sequence generation tasks such as machine ... | We revisit self-training as a semi-supervised learning method for neural sequence generation problem, and show that self-training can be quite successful with injected noise. | 1,381 | scitldr |
We present an end-to-end trained memory system that quickly adapts to new data and generates samples like them. Inspired by Kanerva's sparse distributed memory, it has a robust distributed reading and writing mechanism. The memory is analytically tractable, which enables optimal on-line compression via a Bayesian updat... | A generative memory model that combines slow-learning neural networks and a fast-adapting linear Gaussian model as memory. | 1,382 | scitldr |
Pruning large neural networks while maintaining their performance is often desirable due to the reduced space and time complexity. In existing methods, pruning is done within an iterative optimization procedure with either heuristically designed pruning schedules or additional hyperparameters, undermining their utility... | We present a new approach, SNIP, that is simple, versatile and interpretable; it prunes irrelevant connections for a given task at single-shot prior to training and is applicable to a variety of neural network models without modifications. | 1,383 | scitldr |
Transfer learning uses trained weights from a source model as the initial weightsfor the training of a target dataset. A well chosen source with a large numberof labeled data leads to significant improvement in accuracy. We demonstrate atechnique that automatically labels large unlabeled datasets so that they can train... | A technique for automatically labeling large unlabeled datasets so that they can train source models for transfer learning and its experimental evaluation. | 1,384 | scitldr |
Recent studies in attention modules have enabled higher performance in computer vision tasks by capturing global contexts and accordingly attending important features. In this paper, we propose a simple and highly parametrically efficient module named Tree-structured Attention Module (TAM) which recursively encourages ... | Our paper proposes an attention module which captures inter-channel relationships and offers large performance gains. | 1,385 | scitldr |
A significant challenge for the practical application of reinforcement learning toreal world problems is the need to specify an oracle reward function that correctly defines a task. Inverse reinforcement learning (IRL) seeks to avoid this challenge by instead inferring a reward function from expert behavior. While appe... | The applicability of inverse reinforcement learning is often hampered by the expense of collecting expert demonstrations; this paper seeks to broaden its applicability by incorporating prior task information through meta-learning. | 1,386 | scitldr |
Recent work has focused on combining kernel methods and deep learning. With this in mind, we introduce Deepström networks -- a new architecture of neural networks which we use to replace top dense layers of standard convolutional architectures with an approximation of a kernel function by relying on the Nyström approxi... | A new neural architecture where top dense layers of standard convolutional architectures are replaced with an approximation of a kernel function by relying on the Nyström approximation. | 1,387 | scitldr |
The main goal of this short paper is to inform the neural art community at large on the ethical ramifications of using models trained on the imagenet dataset, or using seed images from classes 445 -n02892767- [’bikini, two-piece’] and 459- n02837789- [’brassiere, bra, bandeau’] of the same. We discovered that many of t... | There's non-consensual and pornographic images in the ImageNet dataset | 1,388 | scitldr |
Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain. It is also challenging to make graph learning inductive and unsupervised at the same time, as learning processes guided by reconstruction error based loss funct... | This paper proposed a novel framework for graph similarity learning in inductive and unsupervised scenario. | 1,389 | scitldr |
Neural population responses to sensory stimuli can exhibit both nonlinear stimulus- dependence and richly structured shared variability. Here, we show how adversarial training can be used to optimize neural encoding models to capture both the deterministic and stochastic components of neural population data. To account... | We show how neural encoding models can be trained to capture both the signal and spiking variability of neural population data using GANs. | 1,390 | scitldr |
A weakly supervised learning based clustering framework is proposed in this paper. As the core of this framework, we introduce a novel multiple instance learning task based on a bag level label called unique class count (ucc), which is the number of unique classes among all instances inside the bag. In this task, no an... | A weakly supervised learning based clustering framework performs comparable to that of fully supervised learning models by exploiting unique class count. | 1,391 | scitldr |
Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample complexity, which hinders their use in real-world domains. Alternatively, model-based reinforcement learning promises to reduce sample comp... | Deep Model-Based RL that works well. | 1,392 | scitldr |
The high computational and parameter complexity of neural networks makes their training very slow and difficult to deploy on energy and storage-constrained comput- ing systems. Many network complexity reduction techniques have been proposed including fixed-point implementation. However, a systematic approach for design... | We analyze and determine the precision requirements for training neural networks when all tensors, including back-propagated signals and weight accumulators, are quantized to fixed-point format. | 1,393 | scitldr |
Machine learning (ML) research has investigated prototypes: examples that are representative of the behavior to be learned. We systematically evaluate five methods for identifying prototypes, both ones previously introduced as well as new ones we propose, finding all of them to provide meaningful but different interpre... | We can identify prototypical and outlier examples in machine learning that are quantifiably very different, and make use of them to improve many aspects of neural networks. | 1,394 | scitldr |
In this work, we propose the Sparse Deep Scattering Croisé Network (SDCSN) a novel architecture based on the Deep Scattering Network (DSN). The DSN is achieved by cascading wavelet transform convolutions with a complex modulus and a time-invariant operator. We extend this work by first, crossing multiple wavelet family... | We propose to enhance the Deep Scattering Network in order to improve control and stability of any given machine learning pipeline by proposing a continuous wavelet thresholding scheme | 1,395 | scitldr |
We propose a neural clustering model that jointly learns both latent features and how they cluster. Unlike similar methods our model does not require a predefined number of clusters. Using a supervised approach, we agglomerate latent features towards randomly sampled targets within the same space whilst progressively r... | Neural clustering without needing a number of clusters | 1,396 | scitldr |
Recent work on explanation generation for decision-making problems has viewed the explanation process as one of model reconciliation where an AI agent brings the human mental model (of its capabilities, beliefs, and goals) to the same page with regards to a task at hand. This formulation succinctly captures many possib... | Model Reconciliation is an established framework for plan explanations, but can be easily hijacked to produce lies. | 1,397 | scitldr |
We consider new variants of optimization algorithms. Our algorithms are based on the observation that mini-batch of stochastic gradients in consecutive iterations do not change drastically and consequently may be predictable. Inspired by the similar setting in online learning literature called Optimistic Online learnin... | We consider new variants of optimization algorithms for training deep nets. | 1,398 | scitldr |
Using Recurrent Neural Networks (RNNs) in sequence modeling tasks is promising in delivering high-quality but challenging to meet stringent latency requirements because of the memory-bound execution pattern of RNNs. We propose a big-little dual-module inference to dynamically skip unnecessary memory access and computat... | We accelerate RNN inference by dynamically reducing redundant memory access using a mixture of accurate and approximate modules. | 1,399 | scitldr |
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