query stringlengths 273 149k | pos stringlengths 18 667 | idx int64 0 1.99k | task_name stringclasses 1
value |
|---|---|---|---|
The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains. Devising a definitive defense against such attacks is proven to be challenging, and the methods relying on detecting adversarial samples are only valid when the att... | A new generative modeling technique based on asymmetrical adversarial training, and its applications to adversarial example detection and robust classification | 500 | scitldr |
Exploration is a key component of successful reinforcement learning, but optimal approaches are computationally intractable, so researchers have focused on hand-designing mechanisms based on exploration bonuses and intrinsic reward, some inspired by curious behavior in natural systems. In this work, we propose a strate... | Meta-learning curiosity algorithms by searching through a rich space of programs yields novel mechanisms that generalize across very different reinforcement-learning domains. | 501 | scitldr |
Many machine learning algorithms represent input data with vector embeddings or discrete codes. When inputs exhibit compositional structure (e.g. objects built from parts or procedures from subroutines), it is natural to ask whether this compositional structure is reflected in the the inputsβ learned representations. W... | This paper proposes a simple procedure for evaluating compositional structure in learned representations, and uses the procedure to explore the role of compositionality in four learning problems. | 502 | scitldr |
In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled constrained programming alg... | A DL model for RNA secondary structure prediction, which uses an unrolled algorithm in the architecture to enforce constraints. | 503 | scitldr |
Learning in recurrent neural networks (RNNs) is most often implemented by gradient descent using backpropagation through time (BPTT), but BPTT does not model accurately how the brain learns. Instead, many experimental on synaptic plasticity can be summarized as three-factor learning rules involving eligibility traces o... | We present eligibility propagation an alternative to BPTT that is compatible with experimental data on synaptic plasticity and competes with BPTT on machine learning benchmarks. | 504 | scitldr |
Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems. RNN policies, however, are particularly difficult to explain, understand, and analyze due to their use of continuous-valued memory vectors and observation features. In ... | Extracting a finite state machine from a recurrent neural network via quantization for the purpose of interpretability with experiments on Atari. | 505 | scitldr |
Building upon the recent success of deep reinforcement learning methods, we investigate the possibility of on-policy reinforcement learning improvement by reusing the data from several consecutive policies. On-policy methods bring many benefits, such as ability to evaluate each ing policy. However, they usually discard... | We investigate the theoretical and practical evidence of on-policy reinforcement learning improvement by reusing the data from several consecutive policies. | 506 | scitldr |
Convolutional Neural Networks (CNN) have been successful in processing data signals that are uniformly sampled in the spatial domain (e.g., images). However, most data signals do not natively exist on a grid, and in the process of being sampled onto a uniform physical grid suffer significant aliasing error and informat... | We use non-Euclidean Fourier Transformation of shapes defined by a simplicial complex for deep learning, achieving significantly better results than point-based sampling techiques used in current 3D learning literature. | 507 | scitldr |
Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences. Traditional score-based casual discovery methods rely on various local heuristics to search for a Directed Acyclic Graph (DAG) according to a predefined score function. While these methods, e.g., greedy equivalenc... | We apply reinforcement learning to score-based causal discovery and achieve promising results on both synthetic and real datasets | 508 | scitldr |
The topic modeling discovers the latent topic probability of given the text documents. To generate the more meaningful topic that better represents the given document, we proposed a universal method which can be used in the data preprocessing stage. The method consists of three steps. First, it generates the word/word-... | We proposed a universal method which can be used in the data preprocessing stage to generate the more meaningful topic that better represents the given document | 509 | scitldr |
Multi-task learning has been successful in modeling multiple related tasks with large, carefully curated labeled datasets. By leveraging the relationships among different tasks, multi-task learning framework can improve the performance significantly. However, most of the existing works are under the assumption that the... | We propose a novel multi-task learning framework which extracts multi-view dependency relationship automatically and use it to guide the knowledge transfer among different tasks. | 510 | scitldr |
We design simple and quantifiable testing of global translation-invariance in deep learning models trained on the MNIST dataset. Experiments on convolutional and capsules neural networks show that both models have poor performance in dealing with global translation-invariance; however, the performance improved by using... | Testing of global translational invariance in Convolutional and Capsule Networks | 511 | scitldr |
Gaussian processes are ubiquitous in nature and engineering. A case in point is a class of neural networks in the infinite-width limit, whose priors correspond to Gaussian processes. Here we perturbatively extend this correspondence to finite-width neural networks, yielding non-Gaussian processes as priors. The methodo... | We develop an analytical method to study Bayesian inference of finite-width neural networks and find that the renormalization-group flow picture naturally emerges. | 512 | scitldr |
Distillation is a method to transfer knowledge from one model to another and often achieves higher accuracy with the same capacity. In this paper, we aim to provide a theoretical understanding on what mainly helps with the distillation. Our answer is "early stopping". Assuming that the teacher network is overparameteri... | theoretically understand the regularization effect of distillation. We show that early stopping is essential in this process. From this perspective, we developed a distillation method for learning with corrupted Label with theoretical guarantees. | 513 | scitldr |
Lifelong learning poses considerable challenges in terms of effectiveness (minimizing prediction errors for all tasks) and overall computational tractability for real-time performance. This paper addresses continuous lifelong multitask learning by jointly re-estimating the inter-task relations (\textit{output} kernel) ... | a novel approach for online lifelong learning using output kernels. | 514 | scitldr |
Minecraft is a videogame that offers many interesting challenges for AI systems. In this paper, we focus in construction scenarios where an agent must build a complex structure made of individual blocks. As higher-level objects are formed of lower-level objects, the construction can naturally be modelled as a hierarchi... | We model a house-construction scenario in Minecraft in classical and HTN planning and compare the advantages and disadvantages of both kinds of models. | 515 | scitldr |
Attacks on natural language models are difficult to compare due to their different definitions of what constitutes a successful attack. We present a taxonomy of constraints to categorize these attacks. For each constraint, we present a real-world use case and a way to measure how well generated samples enforce the cons... | We present a framework for evaluating adversarial examples in natural language processing and demonstrate that generated adversarial examples are often not semantics-preserving, syntactically correct, or non-suspicious. | 516 | scitldr |
In order for machine learning to be deployed and trusted in many applications, it is crucial to be able to reliably explain why the machine learning algorithm makes certain predictions. For example, if an algorithm classifies a given pathology image to be a malignant tumor, then the doctor may need to know which parts ... | Can we trust a neural network's explanation for its prediction? We examine the robustness of several popular notions of interpretability of neural networks including saliency maps and influence functions and design adversarial examples against them. | 517 | scitldr |
Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification. However, existing works are limited to stochastic AUC maximization with a linear predictive model, which restricts its predictive power when dealing with extremely complex data. In this paper, we conside... | The paper designs two algorithms for the stochastic AUC maximization problem with state-of-the-art complexities when using deep neural network as predictive model, which are also verified by empirical studies. | 518 | scitldr |
Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute. The latter is particularly problematic when applying RL to robotics, where detecting whether the desired configuration is reached might require considerable sup... | We tackle goal-conditioned tasks by combining Hindsight Experience Replay and Imitation Learning algorithms, showing faster convergence than the first and higher final performance than the second. | 519 | scitldr |
Bayesian neural networks, which both use the negative log-likelihood loss function and average their predictions using a learned posterior over the parameters, have been used successfully across many scientific fields, partly due to their ability to `effortlessly' extract desired representations from many large-scale d... | We derive a new PAC-Bayesian Bound for unbounded loss functions (e.g. Negative Log-Likelihood). | 520 | scitldr |
Data augmentation techniques, e.g., flipping or cropping, which systematically enlarge the training dataset by explicitly generating more training samples, are effective in improving the generalization performance of deep neural networks. In the supervised setting, a common practice for data augmentation is to assign t... | We propose a simple self-supervised data augmentation technique which improves performance of fully-supervised scenarios including few-shot learning and imbalanced classification. | 521 | scitldr |
Long short-term memory (LSTM) networks allow to exhibit temporal dynamic behavior with feedback connections and seem a natural choice for learning sequences of 3D meshes. We introduce an approach for dynamic mesh representations as used for numerical simulations of car crashes. To bypass the complication of using 3D me... | A two branch LSTM based network architecture learns the representation and dynamics of 3D meshes of numerical crash simulations. | 522 | scitldr |
The purpose of an encoding model is to predict brain activity given a stimulus. In this contribution, we attempt at estimating a whole brain encoding model of auditory perception in a naturalistic stimulation setting. We analyze data from an open dataset, in which 16 subjects watched a short movie while their brain act... | Feature vectors from SoundNet can predict brain activity of subjects watching a movie in auditory and language related brain regions. | 523 | scitldr |
In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions. We use two generative adversarial networks to define the reconstruction and the regularization cost functions of the implicit autoe... | We propose a generative autoencoder that can learn expressive posterior and conditional likelihood distributions using implicit distributions, and train the model using a new formulation of the ELBO. | 524 | scitldr |
Strong inductive biases allow children to learn in fast and adaptable ways. Children use the mutual exclusivity (ME) bias to help disambiguate how words map to referents, assuming that if an object has one label then it does not need another. In this paper, we investigate whether or not standard neural architectures ha... | Children use the mutual exclusivity (ME) bias to learn new words, while standard neural nets show the opposite bias, hindering learning in naturalistic scenarios such as lifelong learning. | 525 | scitldr |
Cortical neurons process and integrate information on multiple timescales. In addition, these timescales or temporal receptive fields display functional and hierarchical organization. For instance, areas important for working memory (WM), such as prefrontal cortex, utilize neurons with stable temporal receptive fields ... | Spiking recurrent neural networks performing a working memory task utilize long heterogeneous timescales, strikingly similar to those observed in prefrontal cortex. | 526 | scitldr |
Conventional deep learning classifiers are static in the sense that they are trained on a predefined set of classes and learning to classify a novel class typically requires re-training. In this work, we address the problem of Low-shot network-expansion learning. We introduce a learning framework which enables expandin... | In this paper, we address the problem of Low-shot network-expansion learning | 527 | scitldr |
People ask questions that are far richer, more informative, and more creative than current AI systems. We propose a neural program generation framework for modeling human question asking, which represents questions as formal programs and generates programs with an encoder-decoder based deep neural network. From extensi... | We introduce a model of human question asking that combines neural networks and symbolic programs, which can learn to generate good questions with or without supervised examples. | 528 | scitldr |
The classification of images taken in special imaging environments except air is the first challenge in extending the applications of deep learning. We report on an UW-Net (Underwater Network), a new convolutional neural network (CNN) based network for underwater image classification. In this model, we simulate the vis... | A visual understanding mechanism for special environment | 529 | scitldr |
Deep networks have achieved impressive across a variety of important tasks. However, a known weakness is a failure to perform well when evaluated on data which differ from the training distribution, even if these differences are very small, as is the case with adversarial examples. We propose \emph{Fortified Networks},... | Better adversarial training by learning to map back to the data manifold with autoencoders in the hidden states. | 530 | scitldr |
Neural networks could misclassify inputs that are slightly different from their training data, which indicates a small margin between their decision boundaries and the training dataset. In this work, we study the binary classification of linearly separable datasets and show that linear classifiers could also have decis... | We show that minimizing the cross-entropy loss by using a gradient method could lead to a very poor margin if the features of the dataset lie on a low-dimensional subspace. | 531 | scitldr |
The concepts of unitary evolution matrices and associative memory have boosted the field of Recurrent Neural Networks (RNN) to state-of-the-art performance in a variety of sequential tasks. However, RNN still has a limited capacity to manipulate long-term memory. To bypass this weakness the most successful applications... | A novel RNN model which outperforms significantly the current frontier of models in a variety of sequential tasks. | 532 | scitldr |
While many recent advances in deep reinforcement learning rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment dynamics. One prospect is to pursue hybrid approaches, as in AlphaGo, which combines Monte-Carlo Tree Search (MCT... | Surprising negative results on Model Based + Model deep RL | 533 | scitldr |
We present a novel architecture of GAN for a disentangled representation learning. The new model architecture is inspired by Information Bottleneck (IB) theory thereby named IB-GAN. IB-GAN objective is similar to that of InfoGAN but has a crucial difference; a capacity regularization for mutual information is adopted, ... | Inspired by Information Bottleneck theory, we propose a new architecture of GAN for a disentangled representation learning | 534 | scitldr |
We investigate the training and performance of generative adversarial networks using the Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical contribution, we clarify the situation with bias in GAN loss functions raised by recent work: we show that gradient estimators used in the optimizat... | Explain bias situation with MMD GANs; MMD GANs work with smaller critic networks than WGAN-GPs; new GAN evaluation metric. | 535 | scitldr |
We extend the Consensus Network framework to Transductive Consensus Network (TCN), a semi-supervised multi-modal classification framework, and identify its two mechanisms: consensus and classification. By putting forward three variants as ablation studies, we show both mechanisms should be functioning together. Overall... | A semi-supervised multi-modal classification framework, TCN, that outperforms various benchmarks. | 536 | scitldr |
Separating mixed distributions is a long standing challenge for machine learning and signal processing. Applications include: single-channel multi-speaker separation (cocktail party problem), singing voice separation and separating reflections from images. Most current methods either rely on making strong assumptions o... | An iterative neural method for extracting signals that are only observed mixed with other signals | 537 | scitldr |
In health, machine learning is increasingly common, yet neural network embedding (representation) learning is arguably under-utilized for physiological signals. This inadequacy stands out in stark contrast to more traditional computer science domains, such as computer vision (CV), and natural language processing (NLP).... | Physiological signal embeddings for prediction performance and hospital transference with a general Shapley value interpretability method for stacked models. | 538 | scitldr |
We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse weights forming the linear combination are known as coefficients. Since the dictionary and coefficients, parameterizing the linear model are ... | We present a provable algorithm for exactly recovering both factors of the dictionary learning model. | 539 | scitldr |
Real-world Relation Extraction (RE) tasks are challenging to deal with, either due to limited training data or class imbalance issues. In this work, we present Data Augmented Relation Extraction (DARE), a simple method to augment training data by properly finetuning GPT2 to generate examples for specific relation types... | Data Augmented Relation Extraction with GPT-2 | 540 | scitldr |
This paper addresses the problem of representing a system's belief using multi-variate normal distributions (MND) where the underlying model is based on a deep neural network (DNN). The major challenge with DNNs is the computational complexity that is needed to obtain model uncertainty using MNDs. To achieve a scalable... | An approximate inference algorithm for deep learning | 541 | scitldr |
The ever-increasing size of modern datasets combined with the difficulty of obtaining label information has made semi-supervised learning of significant practical importance in modern machine learning applications. In comparison to supervised learning, the key difficulty in semi-supervised learning is how to make full ... | We propose a novel manifold regularization strategy based on adversarial training, which can significantly improve the performance of semi-supervised learning. | 542 | scitldr |
Universal approximation property of neural networks is one of the motivations to use these models in various real-world problems. However, this property is not the only characteristic that makes neural networks unique as there is a wide range of other approaches with similar property. Another characteristic which makes... | A novel method for supervised learning through subdivisioning the input space along with function approximation. | 543 | scitldr |
The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods there is currently no study of their robustness to adversarial attacks. We provide the first adversaria... | Adversarial attacks on unsupervised node embeddings based on eigenvalue perturbation theory. | 544 | scitldr |
Hierarchical reinforcement learning methods offer a powerful means of planning flexible behavior in complicated domains. However, learning an appropriate hierarchical decomposition of a domain into subtasks remains a substantial challenge. We present a novel algorithm for subtask discovery, based on the recently introd... | We present a novel algorithm for hierarchical subtask discovery which leverages the multitask linear Markov decision process framework. | 545 | scitldr |
This paper introduces a framework for solving combinatorial optimization problems by learning from input-output examples of optimization problems. We introduce a new memory augmented neural model in which the memory is not resettable (i.e the information stored in the memory after processing an input example is kept fo... | We propose a memory network model to solve Binary LP instances where the memory information is perseved for long-term use. | 546 | scitldr |
Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, w... | We introduce a novel method to train Seq2Seq models with language models that converge faster, generalize better and can almost completely transfer to a new domain using less than 10% of labeled data. | 547 | scitldr |
A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks. Two distinct research paradigms have studied this question. Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adapt... | We introduce the online meta learning problem setting to better capture the spirit and practice of continual lifelong learning. | 548 | scitldr |
We investigate the extent to which individual attention heads in pretrained transformer language models, such as BERT and RoBERTa, implicitly capture syntactic dependency relations. We employ two methodsβtaking the maximum attention weight and computing the maximum spanning treeβto extract implicit dependency relations... | Attention weights don't fully expose what BERT knows about syntax. | 549 | scitldr |
State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high-resolution facial patterns by exploring local appearance knowledge. However, most of these methods do not well exploit facial structures and identity information, and struggle to deal with f... | We propose a novel face super resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures. | 550 | scitldr |
We present Cross-View Training (CVT), a simple but effective method for deep semi-supervised learning. On labeled examples, the model is trained with standard cross-entropy loss. On an unlabeled example, the model first performs inference (acting as a "teacher") to produce soft targets. The model then learns from these... | Self-training with different views of the input gives excellent results for semi-supervised image recognition, sequence tagging, and dependency parsing. | 551 | scitldr |
I show how it can be beneficial to express Metropolis accept/reject decisions in terms of comparison with a uniform value, and to then update this uniform value non-reversibly, as part of the Markov chain state, rather than sampling it independently each iteration. This provides a small improvement for random walk Metr... | A non-reversible way of making accept/reject decisions can be beneficial | 552 | scitldr |
We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES). Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies... | We provide a new framework for MAML in the ES/blackbox setting, and show that it allows deterministic and linear policies, better exploration, and non-differentiable adaptation operators. | 553 | scitldr |
Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning. Some prominent examples are constrained-to-unconstrained transformations of distributions for use in Hamiltonian Monte-Carlo and constructing flexible and learnable densities such as normalizing flows. We... | We present a software framework for transforming distributions and demonstrate its flexibility on relaxing mean-field assumptions in variational inference with the use of coupling flows to replicate structure from the target generative model. | 554 | scitldr |
Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains. Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant experimental bias. This p... | Adversarial Domain adaptation and Multi-domain learning: a new loss to handle multi- and single-domain classes in the semi-supervised setting. | 555 | scitldr |
We introduce our Distribution Regression Network (DRN) which performs regression from input probability distributions to output probability distributions. Compared to existing methods, DRN learns with fewer model parameters and easily extends to multiple input and multiple output distributions. On synthetic and real-wo... | A learning network which generalizes the MLP framework to perform distribution-to-distribution regression | 556 | scitldr |
Existing sequence prediction methods are mostly concerned with time-independent sequences, in which the actual time span between events is irrelevant and the distance between events is simply the difference between their order positions in the sequence. While this time-independent view of sequences is applicable for da... | Proposed methods for time-dependent event representation and regularization for sequence prediction; Evaluated these methods on five datasets that involve a range of sequence prediction tasks. | 557 | scitldr |
Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience can be acquired. This property makes these algorithms appealing for real world problems such as robot control. In practice, however, standa... | We develop a method for stable offline reinforcement learning from logged data. The key is to regularize the RL policy towards a learned "advantage weighted" model of the data. | 558 | scitldr |
One of the main challenges in applying graph convolutional neural networks on gene-interaction data is the lack of understanding of the vector space to which they belong and also the inherent difficulties involved in representing those interactions on a significantly lower dimension, viz Euclidean spaces. The challenge... | This paper presents a deep learning model that combines self-organizing maps and convolutional neural networks for representation learning of multi-omics data | 559 | scitldr |
State-of-the-art performances on language comprehension tasks are achieved by huge language models pre-trained on massive unlabeled text corpora, with very light subsequent fine-tuning in a task-specific supervised manner. It seems the pre-training procedure learns a very good common initialization for further training... | Sparsification as fine-tuning of language models | 560 | scitldr |
We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning. It operates by training an ensemble of policies on the expert demonstration data, and using the variance of their predictions as a cost which is minimized with RL together with a supervised behavioral clon... | Method for addressing covariate shift in imitation learning using ensemble uncertainty | 561 | scitldr |
We present and discuss a simple image preprocessing method for learning disentangled latent factors. In particular, we utilize the implicit inductive bias contained in features from networks pretrained on the ImageNet database. We enhance this bias by explicitly fine-tuning such pretrained networks on tasks useful for ... | We use supervised finetuning of feature vectors to improve transfer from simulation to the real world | 562 | scitldr |
A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy. In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals. In sparse-reward environments, the capacity to e... | We introduce the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended to policy gradient methods. | 563 | scitldr |
Computer vision has undergone a dramatic revolution in performance, driven in large part through deep features trained on large-scale supervised datasets. However, much of these improvements have focused on static image analysis; video understanding has seen rather modest improvements. Even though new datasets and spat... | We propose a new video understanding benchmark, with tasks that by-design require temporal reasoning to be solved, unlike most existing video datasets. | 564 | scitldr |
We address the efficiency issues caused by the straggler effect in the recently emerged federated learning, which collaboratively trains a model on decentralized non-i.i.d. (non-independent and identically distributed) data across massive worker devices without exchanging training data in the unreliable and heterogeneo... | We propose an efficient and robust asynchronous federated learning algorithm on the existence of stragglers | 565 | scitldr |
Long Short-Term Memory (LSTM) units have the ability to memorise and use long-term dependencies between inputs to generate predictions on time series data. We introduce the concept of modifying the cell state (memory) of LSTMs using rotation matrices parametrised by a new set of trainable weights. This addition shows s... | Adding a new set of weights to the LSTM that rotate the cell memory improves performance on some bAbI tasks. | 566 | scitldr |
We address the problem of marginal inference for an exponential family defined over the set of permutation matrices. This problem is known to quickly become intractable as the size of the permutation increases, since its involves the computation of the permanent of a matrix, a #P-hard problem. We introduce Sinkhorn var... | New methodology for variational marginal inference of permutations based on Sinkhorn algorithm, applied to probabilistic identification of neurons | 567 | scitldr |
The robustness of neural networks to adversarial examples has received great attention due to security implications. Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness. In this paper, we provide theoretical j... | We propose the first attack-independent robustness metric, a.k.a CLEVER, that can be applied to any neural network classifier. | 568 | scitldr |
Multi-agent collaboration is required by numerous real-world problems. Although distributed setting is usually adopted by practical systems, local range communication and information aggregation still matter in fulfilling complex tasks. For multi-agent reinforcement learning, many previous studies have been dedicated t... | This paper proposes a spontaneous and self-organizing communication (SSoC) learning scheme for multi-agent RL tasks. | 569 | scitldr |
We study the BERT language representation model and the sequence generation model with BERT encoder for multi-label text classification task. We experiment with both models and explore their special qualities for this setting. We also introduce and examine experimentally a mixed model, which is an ensemble of multi-lab... | On using BERT as an encoder for sequential prediction of labels in multi-label text classification task | 570 | scitldr |
Click Through Rate (CTR) prediction is a critical task in industrial applications, especially for online social and commerce applications. It is challenging to find a proper way to automatically discover the effective cross features in CTR tasks. We propose a novel model for CTR tasks, called Deep neural networks with ... | DNN and Encoder enhanced FM with bilinear attention and max-pooling for CTR | 571 | scitldr |
For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence. In real-world scenarios, future states become increasingly uncertain and multi-modal, particularly on long time horizons. Dropout based Bayesian inference provides a computationally tracta... | Dropout based Bayesian inference is extended to deal with multi-modality and is evaluated on scene anticipation tasks. | 572 | scitldr |
Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision. The major focus so far has been on performance improvement, while there has been little effort in making cGAN more robust to noise. The regression (o... | We introduce a new type of conditional GAN, which aims to leverage structure in the target space of the generator. We augment the generator with a new, unsupervised pathway to learn the target structure. | 573 | scitldr |
Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples. To solve the problem, some regularization adversarial training methods, constraining the output label or logit, have been stu... | In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention. | 574 | scitldr |
A fundamental trait of intelligence is the ability to achieve goals in the face of novel circumstances. In this work, we address one such setting which requires solving a task with a novel set of actions. Empowering machines with this ability requires generalization in the way an agent perceives its available actions a... | We address the problem of generalization of reinforcement learning to unseen action spaces. | 575 | scitldr |
Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating the conditional intensity function. However, parameterizing the intensity function usually incurs several trade-offs. We show how to overcome... | Learn in temporal point processes by modeling the conditional density, not the conditional intensity. | 576 | scitldr |
We propose a novel yet simple neural network architecture for topic modelling. The method is based on training an autoencoder structure where the bottleneck represents the space of the topics distribution and the decoder outputs represent the space of the words distributions over the topics. We exploit an auxiliary dec... | A deep model for topic modelling | 577 | scitldr |
Voice Conversion (VC) is a task of converting perceived speaker identity from a source speaker to a particular target speaker. Earlier approaches in the literature primarily find a mapping between the given source-target speaker-pairs. Developing mapping techniques for many-to-many VC using non-parallel data, including... | Novel adaptive instance normalization based GAN framework for non parallel many-to-many and zero-shot VC. | 578 | scitldr |
Self-attention-based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks. Self attention is able to model long-term dependencies, but it may suffer from the extraction of irrelevant information in the context. To tackle the problem, we propose a novel model ca... | This work propose Sparse Transformer to improve the concentration of attention on the global context through an explicit selection of the most relevant segments for sequence to sequence learning. | 579 | scitldr |
Human observers can learn to recognize new categories of objects from a handful of examples, yet doing so with machine perception remains an open challenge. We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable, as suggested by recent... | Unsupervised representations learned with Contrastive Predictive Coding enable data-efficient image classification. | 580 | scitldr |
We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels. We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to... | Redistributing and growing weights according to the momentum magnitude enables the training of sparse networks from random initializations that can reach dense performance levels with 5% to 50% weights while accelerating training by up to 5.6x. | 581 | scitldr |
To provide principled ways of designing proper Deep Neural Network (DNN) models, it is essential to understand the loss surface of DNNs under realistic assumptions. We introduce interesting aspects for understanding the local minima and overall structure of the loss surface. The parameter domain of the loss surface can... | The loss surface of neural networks is a disjoint union of regions where every local minimum is a global minimum of the corresponding region. | 582 | scitldr |
Contextualized word representations, such as ELMo and BERT, were shown to perform well on a various of semantic and structural (syntactic) task. In this work, we tackle the task of unsupervised disentanglement between semantics and structure in neural language representations: we aim to learn a transformation of the co... | We distill language models representations for syntax by unsupervised metric learning | 583 | scitldr |
We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. The proposed semi-parametric topological memory (SPTM) consists of a (non-parametric) graph with nodes corresponding to locations in the environment and a (parametric) deep network ... | We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. | 584 | scitldr |
The available resolution in our visual world is extremely high, if not infinite. Existing CNNs can be applied in a fully convolutional way to images of arbitrary resolution, but as the size of the input increases, they can not capture contextual information. In addition, computational requirements scale linearly to the... | We propose a novel architecture that traverses an image pyramid in a top-down fashion, while it visits only the most informative regions along the way. | 585 | scitldr |
Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters. We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters ... | We use a hypernetwork to predict optimal weights given hyperparameters, and jointly train everything together. | 586 | scitldr |
Conditional Generative Adversarial Networks (cGANs) are finding increasingly widespread use in many application domains. Despite outstanding progress, quantitative evaluation of such models often involves multiple distinct metrics to assess different desirable properties, such as image quality, conditional consistency,... | We propose a new metric for evaluating conditional GANs that captures image quality, conditional consistency, and intra-conditioning diversity in a single measure. | 587 | scitldr |
Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need t... | We present TreeQN and ATreeC, new architectures for deep reinforcement learning in discrete-action domains that integrate differentiable on-line tree planning into the action-value function or policy. | 588 | scitldr |
Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. Recurrent neural network (RNN) based encoder-decoder models have shown state-of-the-art performance for solving MLC. However, the sequ... | We propose Message Passing Encoder-Decode networks for a fast and accurate way of modelling label dependencies for multi-label classification. | 589 | scitldr |
Recent few-shot learning algorithms have enabled models to quickly adapt to new tasks based on only a few training samples. Previous few-shot learning works have mainly focused on classification and reinforcement learning. In this paper, we propose a few-shot meta-learning system that focuses exclusively on regression ... | We propose a method of doing few-shot regression by learning a set of basis functions to represent the function distribution. | 590 | scitldr |
Large-scale pre-trained language model, such as BERT, has recently achieved great success in a wide range of language understanding tasks. However, it remains an open question how to utilize BERT for text generation tasks. In this paper, we present a novel approach to addressing this challenge in a generic sequence-to-... | We propose a model-agnostic way to leverage BERT for text generation and achieve improvements over Transformer on 2 tasks over 4 datasets. | 591 | scitldr |
Humans have the remarkable ability to correctly classify images despite possible degradation. Many studies have suggested that this hallmark of human vision from the interaction between feedforward signals from bottom-up pathways of the visual cortex and feedback signals provided by top-down pathways. Motivated by such... | CNN-F extends CNN with a feedback generative network for robust vision. | 592 | scitldr |
We develop new approximation and statistical learning theories of convolutional neural networks (CNNs) via the ResNet-type structure where the channel size, filter size, and width are fixed. It is shown that a ResNet-type CNN is a universal approximator and its expression ability is no worse than fully-connected neural... | It is shown that ResNet-type CNNs are a universal approximator and its expression ability is not worse than fully connected neural networks (FNNs) with a \textit{block-sparse} structure even if the size of each layer in the CNN is fixed. | 593 | scitldr |
Few shot image classification aims at learning a classifier from limited labeled data. Generating the classification weights has been applied in many meta-learning approaches for few shot image classification due to its simplicity and effectiveness. However, we argue that it is difficult to generate the exact and unive... | A novel few shot learning method to generate query-specific classification weights via information maximization. | 594 | scitldr |
Conversational question answering (CQA) is a novel QA task that requires the understanding of dialogue context. Different from traditional single-turn machine reading comprehension (MRC), CQA is a comprehensive task comprised of passage reading, coreference resolution, and contextual understanding. In this paper, we pr... | A neural method for conversational question answering with attention mechanism and a novel usage of BERT as contextual embedder | 595 | scitldr |
Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality. However, Wasserstein GANs require the critic to b... | alternative to gradient penalty | 596 | scitldr |
Multi-task learning promises to use less data, parameters, and time than training separate single-task models. But realizing these benefits in practice is challenging. In particular, it is difficult to define a suitable architecture that has enough capacity to support many tasks while not requiring excessive compute fo... | automatic search for multi-task architectures that reduce per-task feature use | 597 | scitldr |
As distributed approaches to natural language semantics have developed and diversified, embedders for linguistic units larger than words (e.g., sentences) have come to play an increasingly important role. To date, such embedders have been evaluated using benchmark tasks (e.g., GLUE) and linguistic probes. We propose a ... | We propose nearest neighbor overlap, a procedure which quantifies similarity between embedders in a task-agnostic manner, and use it to compare 21 sentence embedders. | 598 | scitldr |
Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem. Variational Autoencoders (VAE) on the other hand explicitly maximize a reconstruction-based data log-likelihood forcing it to cover all modes, but suffer from poore... | We propose a new objective for training hybrid VAE-GANs which lead to significant improvement in mode coverage and quality. | 599 | scitldr |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.