query stringlengths 273 149k | pos stringlengths 18 667 | idx int64 0 1.99k | task_name stringclasses 1
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We propose a new approach, known as the iterative regularized dual averaging (iRDA), to improve the efficiency of convolutional neural networks (CNN) by significantly reducing the redundancy of the model without reducing its accuracy. The method has been tested for various data sets, and proven to be significantly more... | A sparse optimization algorithm for deep CNN models. | 1,500 | scitldr |
Learning to imitate expert behavior from demonstrations can be challenging, especially in environments with high-dimensional, continuous observations and unknown dynamics. Supervised learning methods based on behavioral cloning (BC) suffer from distribution shift: because the agent greedily imitates demonstrated action... | A simple and effective alternative to adversarial imitation learning: initialize experience replay buffer with demonstrations, set their reward to +1, set reward for all other data to 0, run Q-learning or soft actor-critic to train. | 1,501 | scitldr |
Generating visualizations and interpretations from high-dimensional data is a common problem in many fields. Two key approaches for tackling this problem are clustering and representation learning. There are very performant deep clustering models on the one hand and interpretable representation learning techniques, oft... | We present a new deep architecture, VarPSOM, and its extension to time series data, VarTPSOM, which achieve superior clustering performance compared to current deep clustering methods on static and temporal data. | 1,502 | scitldr |
Many computer vision applications require solving multiple tasks in real-time. A neural network can be trained to solve multiple tasks simultaneously using'multi-task learning'. This saves computation at inference time as only a single network needs to be evaluated. Unfortunately, this often leads to inferior overall p... | We analyze what tasks are best learned together in one network, and which are best to learn separately. | 1,503 | scitldr |
Search engine has become a fundamental component in various web and mobile applications. Retrieving relevant documents from the massive datasets is challenging for a search engine system, especially when faced with verbose or tail queries. In this paper, we explore a vector space search framework for document retrieval... | A deep semantic framework for textual search engine document retrieval | 1,504 | scitldr |
Semi-Supervised Learning (SSL) approaches have been an influential framework for the usage of unlabeled data when there is not a sufficient amount of labeled data available over the course of training. SSL methods based on Convolutional Neural Networks (CNNs) have recently provided successful on standard benchmark task... | We propose a new algorithm based on the optimal transport to train a CNN in an SSL fashion. | 1,505 | scitldr |
Batch normalization (batch norm) is often used in an attempt to stabilize and accelerate training in deep neural networks. In many cases it indeed decreases the number of parameter updates required to achieve low training error. However, it also reduces robustness to small adversarial input perturbations and noise by d... | Batch normalization reduces adversarial robustness, as well as general robustness in many cases, particularly to noise corruptions. | 1,506 | scitldr |
This paper presents preliminary ideas of our work for auto- mated learning of Hierarchical Goal Networks in nondeter- ministic domains. We are currently implementing the ideas expressed in this paper. Many domains are amenable to hierarchical problem-solving representations whereby complex problems are represented and ... | Learning HGNs, ND domains | 1,507 | scitldr |
Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but... | a simple RNN-based meta-learner that achieves SOTA performance on popular benchmarks | 1,508 | scitldr |
Knowledge Graph Embedding (KGE) has attracted more attention in recent years. Most of KGE models learn from time-unaware triples. However, the inclusion of temporal information beside triples would further improve the performance of a KGE model. In this regard, we propose LiTSE, a temporal KGE model which incorporates ... | Submitted in EMNLP | 1,509 | scitldr |
We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, population-based training with co-play can lead to a progression in agents' behaviors: from rando... | We introduce a new MuJoCo soccer environment for continuous multi-agent reinforcement learning research, and show that population-based training of independent reinforcement learners can learn cooperative behaviors | 1,510 | scitldr |
Program synthesis is the task of automatically generating a program consistent with a specification. Recent years have seen proposal of a number of neural approaches for program synthesis, many of which adopt a sequence generation paradigm similar to neural machine translation, in which sequence-to-sequence models are ... | Using the DSL grammar and reinforcement learning to improve synthesis of programs with complex control flow. | 1,511 | scitldr |
Time series forecasting plays a crucial role in marketing, finance and many other quantitative fields. A large amount of methodologies has been developed on this topic, including ARIMA, Holt–Winters, etc. However, their performance is easily undermined by the existence of change points and anomaly points, two structure... | We propose a novel state space time series model with the capability to capture the structure of change points and anomaly points, so that it has a better forecasting performance when there exist change points and anomalies in the time series. | 1,512 | scitldr |
The complex world around us is inherently multimodal and sequential (continuous). Information is scattered across different modalities and requires multiple continuous sensors to be captured. As machine learning leaps towards better generalization to real world, multimodal sequential learning becomes a fundamental rese... | A multimodal transformer for multimodal sequential learning, with strong empirical results on multimodal language metrics such as multimodal sentiment analysis, emotion recognition and personality traits recognition. | 1,513 | scitldr |
We develop a novel and efficient algorithm for optimizing neural networks inspired by a recently proposed geodesic optimization algorithm. Our algorithm, which we call Stochastic Geodesic Optimization (SGeO), utilizes an adaptive coefficient on top of Polyak's Heavy Ball method effectively controlling the amount of wei... | We utilize an adaptive coefficient on top of regular momentum inspired by geodesic optimization which significantly speeds up training in both convex and non-convex functions. | 1,514 | scitldr |
We introduce the masked translation model (MTM) which combines encoding and decoding of sequences within the same model component. The MTM is based on the idea of masked language modeling and supports both autoregressive and non-autoregressive decoding strategies by simply changing the order of masking. In experiments ... | We use a transformer encoder to do translation by training it in the style of a masked translation model. | 1,515 | scitldr |
We present local ensembles, a method for detecting extrapolation at test time in a pre-trained model. We focus on underdetermination as a key component of extrapolation: we aim to detect when many possible predictions are consistent with the training data and model class. Our method uses local second-order information ... | We present local ensembles, a method for detecting extrapolation in trained models, which approximates the variance of an ensemble using local-second order information. | 1,516 | scitldr |
Coalition operations are essential for responding to the increasing number of world-wide incidents that require large-scale humanitarian assistance. Many nations and non-governmental organizations regularly coordinate to address such problems but their cooperation is often impeded by limits on what information they are... | Privacy can be thought about in the same way as other resources in planning | 1,517 | scitldr |
The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition. Various methods based on variational auto-encoder have been proposed to solve this problem, by enforcing the independence between the representation and modifying the regularization term in the v... | disentangled representation learning | 1,518 | scitldr |
We propose a generative adversarial training approach for the problem of clarification question generation. Our approach generates clarification questions with the goal of eliciting new information that would make the given context more complete. We develop a Generative Adversarial Network (GAN) where the generator is ... | We propose an adversarial training approach to the problem of clarification question generation which uses the answer to the question to model the reward. | 1,519 | scitldr |
Pattern databases are the foundation of some of the strongest admissible heuristics for optimal classical planning. Experiments showed that the most informative way of combining information from multiple pattern databases is to use saturated cost partitioning. Previous work selected patterns and computed saturated cost... | Using saturated cost partitioning to select patterns is preferable to all existing pattern selection algorithms. | 1,520 | scitldr |
State-of-the-art on neural machine translation often use attentional sequence-to-sequence models with some form of convolution or recursion. Vaswani et. al. propose a new architecture that avoids recurrence and convolution completely. Instead, it uses only self-attention and feed-forward layers. While the proposed arch... | Using branched attention with learned combination weights outperforms the baseline transformer for machine translation tasks. | 1,521 | scitldr |
Imitation learning provides an appealing framework for autonomous control: in many tasks, demonstrations of preferred behavior can be readily obtained from human experts, removing the need for costly and potentially dangerous online data collection in the real world. However, policies learned with imitation learning ha... | Hybrid Vision-Driven Imitation Learning and Model-Based Reinforcement Learning for Planning, Forecasting, and Control | 1,522 | scitldr |
Uncertainty estimation and ensembling methods go hand-in-hand. Uncertainty estimation is one of the main benchmarks for assessment of ensembling performance. At the same time, deep learning ensembles have provided state-of-the-art in uncertainty estimation. In this work, we focus on in-domain uncertainty for image clas... | We highlight the problems with common metrics of in-domain uncertainty and perform a broad study of modern ensembling techniques. | 1,523 | scitldr |
Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive . Although most techniques developed so far requires knowledge of the architecture of the machine learning model and remains hard t... | Develop a general framework to establish certified robustness of ML models against various classes of adversarial perturbations | 1,524 | scitldr |
The ability to decompose complex multi-object scenes into meaningful abstractions like objects is fundamental to achieve higher-level cognition. Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability... | We propose a generative latent variable model for unsupervised scene decomposition that provides factorized object representation per foreground object while also decomposing background segments of complex morphology. | 1,525 | scitldr |
We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot". The features may be both real-valued and categorical. Training of the model is performed by stochastic variational Bay... | We propose an extension of conditional variational autoencoder that allows conditioning on an arbitrary subset of the features and sampling the remaining ones. | 1,526 | scitldr |
We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm can discover new architectures with excellent performance. Our approach combines a novel hierarchical genetic representation scheme that imitates the modularized design pattern commonly adopted by human ex... | In this paper we propose a hierarchical architecture representation in which doing random or evolutionary architecture search yields highly competitive results using fewer computational resources than the prior art. | 1,527 | scitldr |
In visual planning (VP), an agent learns to plan goal-directed behavior from observations of a dynamical system obtained offline, e.g., images obtained from self-supervised robot interaction. VP algorithms essentially combine data-driven perception and planning, and are important for robotic manipulation and navigation... | We propose Hallucinative Topological Memory (HTM), a visual planning algorithm that can perform zero-shot long horizon planning in new environments. | 1,528 | scitldr |
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the reduction operations. In this paper, we propose alleviating the BN’s cost by using only a small fraction of data for mean & variance estimation at ... | We propose accelerating Batch Normalization (BN) through sampling less correlated data for reduction operations with regular execution pattern, which achieves up to 2x and 20% speedup for BN itself and the overall training, respectively. | 1,529 | scitldr |
Federated learning allows edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need to store the data in the cloud. We propose Federated matched averaging (FedMA) algorithm designed for federated learning of modern neural ne... | Communication efficient federated learning with layer-wise matching | 1,530 | scitldr |
We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset. SOSELETO is based on the following simple intuition: some source examples are more informative than others for the target problem. To capture this intuition... | Learning with limited training data by exploiting "helpful" instances from a rich data source. | 1,531 | scitldr |
Derivative-free optimization (DFO) using trust region methods is frequently used for machine learning applications, such as (hyper-)parameter optimization without the derivatives of objective functions known. Inspired by the recent work in continuous-time minimizers, our work models the common trust region methods with... | a new derivative-free optimization algorithms derived from Nesterov's accelerated gradient methods and Hamiltonian dynamics | 1,532 | scitldr |
Binarized Neural networks (BNNs) have been shown to be effective in improving network efficiency during the inference phase, after the network has been trained. However, BNNs only binarize the model parameters and activations during propagations. Therefore, BNNs do not offer significant efficiency improvements during t... | Binarized Back-Propagation all you need for completely binarized training is to is to inflate the size of the network | 1,533 | scitldr |
The weight initialization and the activation function of deep neural networks have a crucial impact on the performance of the training procedure. An inappropriate selection can lead to the loss of information of the input during forward propagation and the exponential vanishing/exploding of gradients during back-propag... | How to effectively choose Initialization and Activation function for deep neural networks | 1,534 | scitldr |
The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships. Consequently, our inability to describe these relationships has led to LSTMs being characterized as black boxes. To this end, we introduce contextual decomposition (CD), an interpretation algorithm... | We introduce contextual decompositions, an interpretation algorithm for LSTMs capable of extracting word, phrase and interaction-level importance score | 1,535 | scitldr |
Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction. This is due to the difficulty of estimating the phase of clean speech. To improve speech enhancement performance, we tackle the phase estima... | This paper proposes a novel complex masking method for speech enhancement along with a loss function for efficient phase estimation. | 1,536 | scitldr |
All living organisms struggle against the forces of nature to carve out niches where they can maintain relative stasis. We propose that such a search for order amidst chaos might offer a unifying principle for the emergence of useful behaviors in artificial agents. We formalize this idea into an unsupervised reinforcem... | Learning emergent behavior by minimizing Bayesian surprise with RL in natural environments with entropy. | 1,537 | scitldr |
Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently. A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs. Alternativel... | Deep representations combined with gradient descent can approximate any learning algorithm. | 1,538 | scitldr |
Brain-Computer Interfaces (BCI) may help patients with faltering communication abilities due to neurodegenerative diseases produce text or speech by direct neural processing. However, their practical realization has proven difficult due to limitations in speed, accuracy, and generalizability of existing interfaces. To ... | We present an open-loop brain-machine interface whose performance is unconstrained to the traditionally used bag-of-words approach. | 1,539 | scitldr |
Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option. While previous contributions to feature extraction propose em... | We present a full-network embedding of CNN which outperforms single layer embeddings for transfer learning tasks. | 1,540 | scitldr |
We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds. These thresholds can have fine-grained layer-wise adjustments dynamically via backpropagation. W... | We present a novel network pruning method that can find the optimal sparse structure during the training process with trainable pruning threshold | 1,541 | scitldr |
To which extent can successful machine learning inform our understanding of biological learning? One popular avenue of inquiry in recent years has been to directly map such algorithms into a realistic circuit implementation. Here we focus on learning in recurrent networks and investigate a range of learning algorithms.... | We evaluate new ML learning algorithms' biological plausibility in the abstract based on mathematical operations needed | 1,542 | scitldr |
In recent years several adversarial attacks and defenses have been proposed. Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used. One way out of this dilemma are provable robustness guarantees. While provably robust models for specific $l_p$-perturbation models have been dev... | We introduce a method to train models with provable robustness wrt all the $l_p$-norms for $p\geq 1$ simultaneously. | 1,543 | scitldr |
In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model. The proposed control problem contains a restoration dynamics which is modeled by an RNN. The moving endpoint, which is essentially the terminal time of the assoc... | We propose a novel method to handle image degradations of different levels by learning a diffusion terminal time. Our model can generalize to unseen degradation level and different noise statistic. | 1,544 | scitldr |
The Wasserstein distance received a lot of attention recently in the community of machine learning, especially for its principled way of comparing distributions. It has found numerous applications in several hard problems, such as domain adaptation, dimensionality reduction or generative models. However, its use is sti... | We show that it is possible to fastly approximate Wasserstein distances computation by finding an appropriate embedding where Euclidean distance emulates the Wasserstein distance | 1,545 | scitldr |
Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of capturing the word order. The reason is that the computation of CB... | We present a novel training scheme for efficiently obtaining order-aware sentence representations. | 1,546 | scitldr |
This paper proposes Metagross (Meta Gated Recursive Controller), a new neural sequence modeling unit. Our proposed unit is characterized by recursive parameterization of its gating functions, i.e., gating mechanisms of Metagross are controlled by instances of itself, which are repeatedly called in a recursive fashion. ... | Recursive Parameterization of Recurrent Models improve performance | 1,547 | scitldr |
Which generative model is the most suitable for Continual Learning? This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks. We investigate how several models learn and forget, considering various strategies: rehearsal, regularization, generative replay and fine-tunin... | A comparative study of generative models on Continual Learning scenarios. | 1,548 | scitldr |
We propose a new sample-efficient methodology, called Supervised Policy Update (SPU), for deep reinforcement learning. Starting with data generated by the current policy, SPU formulates and solves a constrained optimization problem in the non-parameterized proximal policy space. Using supervised regression, it then con... | first posing and solving the sample efficiency optimization problem in the non-parameterized policy space, and then solving a supervised regression problem to find a parameterized policy that is near the optimal non-parameterized policy. | 1,549 | scitldr |
Recent work on modeling neural responses in the primate visual system has benefited from deep neural networks trained on large-scale object recognition, and found a hierarchical correspondence between layers of the artificial neural network and brain areas along the ventral visual stream. However, we neither know wheth... | A goal-driven approach to model four mouse visual areas (V1, LM, AL, RL) based on deep neural networks trained on static object recognition does not unveil a functional organization of visual cortex unlike in primates | 1,550 | scitldr |
The reparameterization trick has become one of the most useful tools in the field of variational inference. However, the reparameterization trick is based on the standardization transformation which restricts the scope of application of this method to distributions that have tractable inverse cumulative distribution fu... | a generalized transformation-based gradient model for variational inference | 1,551 | scitldr |
To simultaneously capture syntax and semantics from a text corpus, we propose a new larger-context language model that extracts recurrent hierarchical semantic structure via a dynamic deep topic model to guide natural language generation. Moving beyond a conventional language model that ignores long-range word dependen... | We introduce a novel larger-context language model to simultaneously captures syntax and semantics, making it capable of generating highly interpretable sentences and paragraphs | 1,552 | scitldr |
We present a novel approach to train a natural media painting using reinforcement learning. Given a reference image, our formulation is based on stroke-based rendering that imitates human drawing and can be learned from scratch without supervision. Our painting agent computes a sequence of actions that represent the pr... | We train a natural media painting agent using environment model. Based on our painting agent, we present a novel approach to train a constrained painting agent that follows the command encoded in the observation. | 1,553 | scitldr |
Delusional bias is a fundamental source of error in approximate Q-learning. To date, the only techniques that explicitly address delusion require comprehensive search using tabular value estimates. In this paper, we develop efficient methods to mitigate delusional bias by training Q-approximators with labels that are "... | We developed a search framework and consistency penalty to mitigate delusional bias. | 1,554 | scitldr |
The paper proposes and demonstrates a Deep Convolutional Neural Network (DCNN) architecture to identify users with disguised face attempting a fraudulent ATM transaction. The recent introduction of Disguised Face Identification (DFI) framework proves the applicability of deep neural networks for this very problem. All ... | Proposed System can prevent impersonators with facial disguises from completing a fraudulent transaction using a pre-trained DCNN. | 1,555 | scitldr |
Auto-encoders are commonly used for unsupervised representation learning and for pre-training deeper neural networks. When its activation function is linear and the encoding dimension (width of hidden layer) is smaller than the input dimension, it is well known that auto-encoder is optimized to learn the principal comp... | theoretical analysis of nonlinear wide autoencoder | 1,556 | scitldr |
Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions. In order to realize this potential of faster lea... | We introduce the first Hierarchical RL approach to successfully learn 3-level hierarchies in parallel in tasks with continuous state and action spaces. | 1,557 | scitldr |
Positive-unlabeled (PU) learning addresses the problem of learning a binary classifier from positive (P) and unlabeled (U) data. It is often applied to situations where negative (N) data are difficult to be fully labeled. However, collecting a non-representative N set that contains only a small portion of all possible ... | This paper studied the PUbN classification problem, where we incorporate biased negative (bN) data, i.e., negative data that is not fully representative of the true underlying negative distribution, into positive-unlabeled (PU) learning. | 1,558 | scitldr |
Reinforcement learning (RL) is frequently used to increase performance in text generation tasks, including machine translation (MT), notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN). However, little is known about what and how these methods learn in the context of MT. We ... | Reinforcment practices for machine translation performance gains might not come from better predictions. | 1,559 | scitldr |
Significant advances have been made in Natural Language Processing (NLP) modelling since the beginning of 2018. The new approaches allow for accurate , even when there is little labelled data, because these NLP models can benefit from training on both task-agnostic and task-specific unlabelled data. However, these adva... | We train a small, efficient CNN with the same performance as the OpenAI Transformer on text classification tasks | 1,560 | scitldr |
Combining multiple function approximators in machine learning models typically leads to better performance and robustness compared with a single function. In reinforcement learning, ensemble algorithms such as an averaging method and a majority voting method are not always optimal, because each function can learn funda... | Ensemble method for reinforcement learning that weights Q-functions based on accumulated TD errors. | 1,561 | scitldr |
This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key is... | Bsuite is a collection of carefully-designed experiments that investigate the core capabilities of RL agents. | 1,562 | scitldr |
Sequence-to-sequence attention-based models are a promising approach for end-to-end speech recognition. The increased model power makes the training procedure more difficult, and analyzing failure modes of these models becomes harder because of the end-to-end nature. In this work, we present various analyses to better ... | improved pretraining, and analysing encoder output and attention | 1,563 | scitldr |
Validation is a key challenge in the search for safe autonomy. Simulations are often either too simple to provide robust validation, or too complex to tractably compute. Therefore, approximate validation methods are needed to tractably find failures without unsafe simplifications. This paper presents the theory behind ... | A formulation for a black-box, reinforcement learning method to find the most-likely failure of a system acting in complex scenarios. | 1,564 | scitldr |
Multi-step greedy policies have been extensively used in model-based Reinforcement Learning (RL) and in the case when a model of the environment is available (e.g., in the game of Go). In this work, we explore the benefits of multi-step greedy policies in model-free RL when employed in the framework of multi-step Dynam... | Use model free algorithms like DQN/TRPO to solve short horizon problems (model free) iteratively in a Policy/Value Iteration fashion. | 1,565 | scitldr |
The adversarial training procedure proposed by is one of the most effective methods to defend against adversarial examples in deep neural net- works (DNNs). In our paper, we shed some lights on the practicality and the hardness of adversarial training by showing that the effectiveness (robustness on test set) of advers... | We show that even the strongest adversarial training methods cannot defend against adversarial examples crafted on slightly scaled and shifted test images. | 1,566 | scitldr |
Edge intelligence especially binary neural network (BNN) has attracted considerable attention of the artificial intelligence community recently. BNNs significantly reduce the computational cost, model size, and memory footprint. However, there is still a performance gap between the successful full-precision neural netw... | Improve saturating activations (sigmoid, tanh, htanh etc.) and Binarized Neural Network with Bias Initialization | 1,567 | scitldr |
Understanding how people represent categories is a core problem in cognitive science, with the flexibility of human learning remaining a gold standard to which modern artificial intelligence and machine learning aspire. Decades of psychological research have yielded a variety of formal theories of categories, yet valid... | using deep neural networks and clever algorithms to capture human mental visual concepts | 1,568 | scitldr |
The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fool... | We present a novel black-box targeted attack that is able to fool state of the art speech to text transcription. | 1,569 | scitldr |
Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement learning. However, have mostly been demonstrated on imitating a single distinct motion pattern, and do not generalize to interactive tasks that ... | Synthesizing human motions on interactive tasks using mocap data and hierarchical RL. | 1,570 | scitldr |
We propose studying GAN training dynamics as regret minimization, which is in contrast to the popular view that there is consistent minimization of a divergence between real and generated distributions. We analyze the convergence of GAN training from this new point of view to understand why mode collapse happens. We hy... | Analysis of convergence and mode collapse by studying GAN training process as regret minimization | 1,571 | scitldr |
Deep neural networks (DNNs) have attained surprising achievement during the last decade due to the advantages of automatic feature learning and freedom of expressiveness. However, their interpretability remains mysterious because DNNs are complex combinations of linear and nonlinear transformations. Even though many mo... | We propose a novel framework to evaluate the interpretability of neural network. | 1,572 | scitldr |
Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. The discount factor values future rewards by an exponential scheme that leads to theoretical convergence guarantees of the Bellman equation. However, evidence from psychology, economics and neuroscience suggests that... | A deep RL agent that learns hyperbolic (and other non-exponential) Q-values and a new multi-horizon auxiliary task. | 1,573 | scitldr |
Emotion is playing a great role in our daily lives. The necessity and importance of an automatic Emotion recognition system is getting increased. Traditional approaches of emotion recognition are based on facial images, measurements of heart rates, blood pressure, temperatures, tones of voice/speech, etc. However, thes... | This paper presents EEG based emotion detection of a person towards an image stimuli and its applicability on neuromarketing. | 1,574 | scitldr |
We present a probabilistic framework for session based recommendation. A latent variable for the user state is updated as the user views more items and we learn more about their interests. We provide computational solutions using both the re-parameterization trick and using the Bouchard bound for the softmax function, ... | Fast variational approximations for approximating a user state and learning product embeddings | 1,575 | scitldr |
An important question in task transfer learning is to determine task transferability, i.e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task. Typically, transferability is either measured experimentally or inferred through task relatedne... | We present a provable and easily-computable evaluation function that estimates the performance of transferred representations from one learning task to another in task transfer learning. | 1,576 | scitldr |
This paper presents a generic framework to tackle the crucial class mismatch problem in unsupervised domain adaptation (UDA) for multi-class distributions. Previous adversarial learning methods condition domain alignment only on pseudo labels, but noisy and inaccurate pseudo labels may perturb the multi-class distribut... | We propose a reliable conditional adversarial learning scheme along with a simple, generic yet effective framework for UDA tasks. | 1,577 | scitldr |
We present a new methodology that constructs a family of \emph{positive definite kernels} from any given dissimilarity measure on structured inputs whose elements are either real-valued time series or discrete structures such as strings, histograms, and graphs. Our approach, which we call D2KE (from Distance to Kernel ... | From Distance to Kernel and Embedding via Random Features For Structured Inputs | 1,578 | scitldr |
Deep Convolution Neural Networks (CNNs), rooted by the pioneer work of \cite{Hinton1986,LeCun1985,Alex2012}, and summarized in \cite{LeCunBengioHinton2015}, have been shown to be very useful in a variety of fields. The state-of-the art CNN machines such as image rest net \cite{He_2016_CVPR} are described by real value ... | A quantum inspired kernel for convolution network, exhibiting interference phenomena, can be very useful (and compared it with real value counterpart). | 1,579 | scitldr |
We present an artificial intelligence research platform inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMOs). We demonstrate how this platform can be used to study behavior and learning in large populations of neural agents. Unlike currently popular game environment... | An MMO-inspired research game platform for studying emergent behaviors of large populations in a complex environment | 1,580 | scitldr |
In this paper, we propose an improved quantitative evaluation framework for Generative Adversarial Networks (GANs) on generating domain-specific images, where we improve conventional evaluation methods on two levels: the feature representation and the evaluation metric. Unlike most existing evaluation frameworks which ... | This paper improves existing sample-based evaluation for GANs and contains some insightful experiments. | 1,581 | scitldr |
Recent efforts on training light-weight binary neural networks offer promising execution/memory efficiency. This paper introduces ResBinNet, which is a composition of two interlinked methodologies aiming to address the slow convergence speed and limited accuracy of binary convolutional neural networks. The first method... | Residual Binary Neural Networks significantly improve the convergence rate and inference accuracy of the binary neural networks. | 1,582 | scitldr |
In real-world machine learning applications, large outliers and pervasive noise are commonplace, and access to clean training data as required by standard deep autoencoders is unlikely. Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillanc... | Unsupervised method to detect adversarial samples in autoencoder's activations and reconstruction error space | 1,583 | scitldr |
Learning knowledge graph embeddings (KGEs) is an efficient approach to knowledge graph completion. Conventional KGEs often suffer from limited knowledge representation, which causes less accuracy especially when training on sparse knowledge graphs. To remedy this, we present Pretrain-KGEs, a training framework for lear... | We propose to learn knowledgeable entity and relation representations from Bert for knowledge graph embeddings. | 1,584 | scitldr |
We describe a novel way of representing a symbolic knowledge base (KB) called a sparse-matrix reified KB. This representation enables neural modules that are fully differentiable, faithful to the original semantics of the KB, expressive enough to model multi-hop inferences, and scalable enough to use with realistically... | A scalable differentiable neural module that implements reasoning on symbolic KBs. | 1,585 | scitldr |
We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant (DCEM) that enables us to differentiate the output of CEM with respect to the objective function's parameters. In the machine learning setting this brings C... | DCEM learns latent domains for optimization problems and helps bridge the gap between model-based and model-free RL --- we create a differentiable controller and fine-tune parts of it with PPO | 1,586 | scitldr |
We propose a new framework for entity and event extraction based on generative adversarial imitation learning -- an inverse reinforcement learning method using generative adversarial network (GAN). We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expec... | We use dynamic rewards to train event extractors. | 1,587 | scitldr |
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We ... | GANs benefit from scaling up. | 1,588 | scitldr |
In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation. Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks. Furthermore, provide an upper bound for target error when transferring the knowle... | joint error matters for unsupervised domain adaptation especially when the domain shift is huge | 1,589 | scitldr |
A zoo of deep nets is available these days for almost any given task, and it is increasingly unclear which net to start with when addressing a new task, or which net to use as an initialization for fine-tuning a new model. To address this issue, in this paper, we develop knowledge flow which moves ‘knowledge’ from mult... | ‘Knowledge Flow’ trains a deep net (student) by injecting information from multiple nets (teachers). The student is independent upon training and performs very well on learned tasks irrespective of the setting (reinforcement or supervised learning). | 1,590 | scitldr |
Despite the impressive performance of deep neural networks (DNNs) on numerous learning tasks, they still exhibit uncouth behaviours. One puzzling behaviour is the subtle sensitive reaction of DNNs to various noise attacks. Such a nuisance has strengthened the line of research around developing and training noise-robust... | An efficient estimate to the Gaussian first moment of DNNs as a regularizer to training robust networks. | 1,591 | scitldr |
Reading comprehension is a challenging task, especially when executed across longer or across multiple evidence documents, where the answer is likely to reoccur. Existing neural architectures typically do not scale to the entire evidence, and hence, resort to selecting a single passage in the document (either via trunc... | We propose neural cascades, a simple and trivially parallelizable approach to reading comprehension, consisting only of feed-forward nets and attention that achieves state-of-the-art performance on the TriviaQA dataset. | 1,592 | scitldr |
Compressed forms of deep neural networks are essential in deploying large-scale computational models on resource-constrained devices. Contrary to analogous domains where large-scale systems are build as a hierarchical repetition of small- scale units, the current practice in Machine Learning largely relies on models wi... | We advance the state-of-the-art in model compression by proposing Atomic Compression Networks (ACNs), a novel architecture that is constructed by recursive repetition of a small set of neurons. | 1,593 | scitldr |
Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions. However, a central challenge in video prediction is that the future is highly uncertain: a sequence of past observations of events can imply many possi... | We demonstrate that flow-based generative models offer a viable and competitive approach to generative modeling of video. | 1,594 | scitldr |
Understanding the behavior of stochastic gradient descent (SGD) in the context of deep neural networks has raised lots of concerns recently. Along this line, we theoretically study a general form of gradient based optimization dynamics with unbiased noise, which unifies SGD and standard Langevin dynamics. Through inves... | We provide theoretical and empirical analysis on the role of anisotropic noise introduced by stochastic gradient on escaping from minima. | 1,595 | scitldr |
Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning. In this paper, we show how to make more effective use of the model by exploiting its differentiability. We construct a policy optimization a... | Policy gradient through backpropagation through time using learned models and Q-functions. SOTA results in reinforcement learning benchmark environments. | 1,596 | scitldr |
Meta-learning algorithms learn to acquire new tasks more quickly from past experience. In the context of reinforcement learning, meta-learning algorithms can acquire reinforcement learning procedures to solve new problems more efficiently by utilizing experience from prior tasks. The performance of meta-learning algori... | Meta-learning on self-proposed task distributions to speed up reinforcement learning without human specified task distributions | 1,597 | scitldr |
We introduce a novel method that enables parameter-efficient transfer and multi-task learning with deep neural networks. The basic approach is to learn a model patch - a small set of parameters - that will specialize to each task, instead of fine-tuning the last layer or the entire network. For instance, we show that l... | A novel and practically effective method to adapt pretrained neural networks to new tasks by retraining a minimal (e.g., less than 2%) number of parameters | 1,598 | scitldr |
Adversarial examples have somewhat disrupted the enormous success of machine learning (ML) and are causing concern with regards to its trustworthiness: A small perturbation of an input in an arbitrary failure of an otherwise seemingly well-trained ML system. While studies are being conducted to discover the intrinsic p... | A new theoretical explanation for the existence of adversarial examples | 1,599 | scitldr |
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