source stringlengths 200 2.98k | target stringlengths 18 668 |
|---|---|
It has been argued that the brain is a prediction machine that continuously learns how to make better predictions about the stimuli received from the external environment. For this purpose, it builds a model of the world around us and uses this model to infer the external stimulus. Predictive coding has been proposed a... | A predictive coding based learning algorithm for building deep neural network models of the brain |
In this paper, we propose deep convolutional generative adversarial networks (DCGAN) that learn to produce a 'mental image' of the input image as internal representation of a certain category of input data distribution. This mental image is what the DCGAN 'imagines' that the input image might look like under ideal co... | Object instance recognition with adversarial autoencoders was performed with a novel 'mental image' target that is canonical representation of the input image. |
An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large number of interactions with the environment in order to master a skill. The learned skill usually generalizes poorly across domains and re-training is often necessary when presented with a new ta... | Combine temporal logic with hierarchical reinforcement learning for skill composition |
The tremendous memory and computational complexity of Convolutional Neural Networks (CNNs) prevents the inference deployment on resource-constrained systems. As a result, recent research focused on CNN optimization techniques, in particular quantization, which allows weights and activations of layers to be represented ... | We propose a quantization scheme for weights and activations of deep neural networks. This reduces the memory footprint substantially and accelerates inference. |
Reinforcement learning (RL) agents optimize only the features specified in a reward function and are indifferent to anything left out inadvertently. This means that we must not only specify what to do, but also the much larger space of what not to do. It is easy to forget these preferences, since these preferences are ... | When a robot is deployed in an environment that humans have been acting in, the state of the environment is already optimized for what humans want, and we can use this to infer human preferences. |
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a novel, systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affec... | Systematic categorization of regularization methods for deep learning, revealing their similarities. |
Deep neural networks are surprisingly efficient at solving practical tasks,
but the theory behind this phenomenon is only starting to catch up with
the practice. Numerous works show that depth is the key to this efficiency.
A certain class of deep convolutional networks – namely those that correspond
to the Hierarc... | We prove the exponential efficiency of recurrent-type neural networks over shallow networks. |
Probabilistic modelling is a principled framework to perform model aggregation, which has been a primary mechanism to combat mode collapse in the context of Generative Adversarial Networks (GAN). In this paper, we propose a novel probabilistic framework for GANs, ProbGAN, which iteratively learns a distribution over ge... | A novel probabilistic treatment for GAN with theoretical guarantee. |
In the adversarial-perturbation problem of neural networks, an adversary starts with a neural network model $F$ and a point $\bfx$ that $F$ classifies correctly, and applies a \emph{small perturbation} to $\bfx$ to produce another point $\bfx'$ that $F$ classifies \emph{incorrectly}. In this paper, we propose taking ... | Defending against adversarial perturbations of neural networks from manifold assumption |
Recently Neural Architecture Search (NAS) has aroused great interest in both academia and industry, however it remains challenging because of its huge and non-continuous search space. Instead of applying evolutionary algorithm or reinforcement learning as previous works, this paper proposes a Direct Sparse Optimization... | single shot neural architecture search via direct sparse optimization |
Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. Thi... | Obtains state-of-the-art accuracy for quantized, shallow nets by leveraging distillation. |
Previous work has demonstrated the benefits of incorporating additional linguistic annotations such as syntactic trees into neural machine translation. However the cost of obtaining those syntactic annotations is expensive for many languages and the quality of unsupervised learning linguistic structures is too poor to ... | improve NMT with latent trees |
Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a... | Learn by working backwards from a single demonstration, even an inefficient one, and progressively have the agent do more of the solving itself. |
Episodic memory is a psychology term which refers to the ability to recall specific events from the past. We suggest one advantage of this particular type of memory is the ability to easily assign credit to a specific state when remembered information is found to be useful. Inspired by this idea, and the increasing pop... | External memory for online reinforcement learning based on estimating gradients over a novel reservoir sampling technique. |
We achieve bias-variance decomposition for Boltzmann machines using an information geometric formulation. Our decomposition leads to an interesting phenomenon that the variance does not necessarily increase when more parameters are included in Boltzmann machines, while the bias always decreases. Our result gives a theo... | We achieve bias-variance decomposition for Boltzmann machines using an information geometric formulation. |
Recurrent Neural Networks (RNNs) are powerful tools for solving sequence-based problems, but their efficacy and execution time are dependent on the size of the network. Following recent work in simplifying these networks with model pruning and a novel mapping of work onto GPUs, we design an efficient implementation f... | Combining network pruning and persistent kernels into a practical, fast, and accurate network implementation. |
Weight pruning has proven to be an effective method in reducing the model size and computation cost while not sacrificing the model accuracy. Conventional sparse matrix formats, however, involve irregular index structures with large storage requirement and sequential reconstruction process, resulting in inefficient use... | We present a new pruning method and sparse matrix format to enable high index compression ratio and parallel index decoding process. |
Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to emplo... | A novel hierarchical policy network which can reuse previously learned skills alongside and as subcomponents of new skills by discovering the underlying relations between skills. |
Embeddings are a fundamental component of many modern machine learning and natural language processing models.
Understanding them and visualizing them is essential for gathering insights about the information they capture and the behavior of the models.
State of the art in analyzing embeddings consists in projecting ... | We propose to use explicit vector algebraic formulae projection as an alternative way to visualize embedding spaces specifically tailored for goal-oriented analysis tasks and it outperforms t-SNE in our user study. |
Learning deep networks which can resist large variations between training andtesting data is essential to build accurate and robust image classifiers. Towardsthis end, a typical strategy is to apply data augmentation to enlarge the trainingset. However, standard data augmentation is essentially a brute-forc... | We propose a principled approach that endows classifiers with the ability to resist larger variations between training and testing data in an intelligent and efficient manner. |
It is well known that it is possible to construct "adversarial examples"
for neural networks: inputs which are misclassified by the network
yet indistinguishable from true data. We propose a simple
modification to standard neural network architectures, thermometer
encoding, which significantly increases the robustn... | Input discretization leads to robustness against adversarial examples |
Low-precision training is a promising way of decreasing the time and energy cost of training machine learning models.
Previous work has analyzed low-precision training algorithms, such as low-precision stochastic gradient descent, and derived theoretical bounds on their convergence rates.
These bounds tend to depend ... | we proved dimension-independent bounds for low-precision training algorithms |
We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and ERL2. Results are presented on a novel environment we call 'Krazy World' and a set of maze environments. We show E-MAML and ERL2 deliver better performance on tasks where expl... | Modifications to MAML and RL2 that should allow for better exploration. |
We propose a new class of probabilistic neural-symbolic models for visual question answering (VQA) that provide interpretable explanations of their decision making in the form of programs, given a small annotated set of human programs. The key idea of our approach is to learn a rich latent space which effectively propa... | A probabilistic neural symbolic model with a latent program space, for more interpretable question answering |
The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network. In recent years, several techniques have been proposed for training networks that are robust to such examples; and ea... | We use formal verification to assess the effectiveness of techniques for finding adversarial examples or for defending against adversarial examples. |
This paper introduces a new framework for open-domain question answering in which the retriever and the reader \emph{iteratively interact} with each other. The framework is agnostic to the architecture of the machine reading model provided it has \emph{access} to the token-level hidden representations of the reader. Th... | Paragraph retriever and machine reader interacts with each other via reinforcement learning to yield large improvements on open domain datasets |
Many imaging tasks require global information about all pixels in an image. Conventional bottom-up classification networks globalize information by decreasing resolution; features are pooled and down-sampled into a single output. But for semantic segmentation and object detection tasks, a network must provide higher-re... | Presents new architecture which leverages information globalization power of u-nets in a deeper networks and performs well across tasks without any bells and whistles. |
Asking questions is an important ability for a chatbot. This paper focuses on question generation. Although there are existing works on question generation based on a piece of descriptive text, it remains to be a very challenging problem. In the paper, we propose a new question generation problem, which also requires t... | We propose a neural network that is able to generate topic-specific questions. |
Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option
to restore voluntary movements after paralysis. These devices are based on the
ability to extract information about movement intent from neural signals recorded
using multi-electrode arrays chronically implanted in the motor cortices... | We implement an adversarial domain adaptation network to stabilize a fixed Brain-Machine Interface against gradual changes in the recorded neural signals. |
Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-s... | Analysing and understanding how neural network agents learn to understand simple grounded language |
Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from... | Learning object parts, hierarchical structure, and dynamics by watching how they move |
A successful application of convolutional architectures is to increase the resolution of single low-resolution images -- a image restoration task called super-resolution (SR). Naturally, SR is of value to resource constrained devices like mobile phones, electronic photograph frames and televisions to enhance image qual... | We build an understanding of resource-efficient techniques on Super-Resolution |
Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with 100% accuracy. In this work we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased tow... | We investigate ReLU networks in the Fourier domain and demonstrate peculiar behaviour. |
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a single point in the embedding space. Often the distance between points is used as a proxy for match confi... | The paper proposes using probability distributions instead of points for instance embeddings tasks such as recognition and verification. |
Convolution neural networks typically consist of many convolutional layers followed by several fully-connected layers. While convolutional layers map between high-order activation tensors, the fully-connected layers operate on flattened activation vectors. Despite its success, this approach has notable drawbacks. F... | We propose tensor contraction and low-rank tensor regression layers to preserve and leverage the multi-linear structure throughout the network, resulting in huge space savings with little to no impact on performance. |
We explore ways of incorporating bilingual dictionaries to enable semi-supervised
neural machine translation. Conventional back-translation methods have shown
success in leveraging target side monolingual data. However, since the quality of
back-translation models is tied to the size of the available parallel corpor... | We use bilingual dictionaries for data augmentation for neural machine translation |
Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, obs... | We propose a novel model of curiosity based on episodic memory and the ideas of reachability which allows us to overcome the known "couch-potato" issues of prior work. |
We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task. We use this task to compare a series of architectures which are ubiquitous in the sequence-processing literature, in addition ... | We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task. |
Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to accommodate new, previously unseen data demands high computational time and energy requirements. Also, previously seen training samples ma... | The paper is about a new energy-efficient methodology for Incremental learning |
Recurrent neural networks (RNNs) are widely used to model sequential data but
their non-linear dependencies between sequence elements prevent parallelizing
training over sequence length. We show the training of RNNs with only linear
sequential dependencies can be parallelized over the sequence length using the
para... | use parallel scan to parallelize linear recurrent neural nets. train model on length 1 million dependency |
Neural text generation models are often autoregressive language models or seq2seq models. Neural autoregressive and seq2seq models that generate text by sampling words sequentially, with each word conditioned on the previous model, are state-of-the-art for several machine translation and summarization benchmarks. These... | Natural language GAN for filling in the blank |
Parametric texture models have been applied successfully to synthesize artificial images. Psychophysical studies show that under defined conditions observers are unable to differentiate between model-generated and original natural textures. In industrial applications the reverse case is of interest: a texture analysis ... | Comparison of psychophysical and CNN-encoded texture representations in a one-class neural network novelty detection application. |
In representation learning (RL), how to make the learned representations easy to interpret and less overfitted to training data are two important but challenging issues. To address these problems, we study a new type of regularization approach that encourages the supports of weight vectors in RL models to have small ov... | We propose a new type of regularization approach that encourages non-overlapness in representation learning, for the sake of improving interpretability and reducing overfitting. |
Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use \emph{ad hoc} gating mechanisms. Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues.
We prove that learnabl... | Proves that gating mechanisms provide invariance to time transformations. Introduces and tests a new initialization for LSTMs from this insight. |
Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations. A good teacher will select appropriate teaching materials, impact suitable methodologies, and set up targeted examinations, according to the learning behaviors of the students. In the field of artificia... | We propose and verify the effectiveness of learning to teach, a new framework to automatically guide machine learning process. |
We present DL2, a system for training and querying neural networks with logical constraints. The key idea is to translate these constraints into a differentiable loss with desirable mathematical properties and to then either train with this loss in an iterative manner or to use the loss for querying the network for inp... | A differentiable loss for logic constraints for training and querying neural networks. |
Genetic algorithms have been widely used in many practical optimization problems.
Inspired by natural selection, operators, including mutation, crossover
and selection, provide effective heuristics for search and black-box optimization.
However, they have not been shown useful for deep reinforcement learning, possib... | Genetic algorithms based approach for optimizing deep neural network policies |
To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common technique for quantizing neural networks is the straight-through gradient method, which enables back-propagation through the quantization ma... | A principled framework for model quantization using the proximal gradient method, with empirical evaluation and theoretical convergence analyses. |
Background: Statistical mechanics results (Dauphin et al. (2014); Choromanska et al. (2015)) suggest that local minima with high error are exponentially rare in high dimensions. However, to prove low error guarantees for Multilayer Neural Networks (MNNs), previous works so far required either a heavily modified MNN mod... | "Bad" local minima are vanishing in a multilayer neural net: a proof with more reasonable assumptions than before |
Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is their good transferability, making black-box attacks feasible in real-world applications. Due to the threat of adversarial attacks, many met... | We propose an attention-invariant attack method to generate more transferable adversarial examples for black-box attacks, which can fool state-of-the-art defenses with a high success rate. |
We present Merged-Averaged Classifiers via Hashing (MACH) for $K$-classification with large $K$. Compared to traditional one-vs-all classifiers that require $O(Kd)$ memory and inference cost, MACH only need $O(d\log{K})$ memory while only requiring $O(K\log{K} + d\log{K})$ operation for inference. MACH is the first gen... | How to Training 100,000 classes on a single GPU |
Gradient-based optimization is the foundation of deep learning and reinforcement learning.
Even when the mechanism being optimized is unknown or not differentiable, optimization using high-variance or biased gradient estimates is still often the best strategy. We introduce a general framework for learning low-variance... | We present a general method for unbiased estimation of gradients of black-box functions of random variables. We apply this method to discrete variational inference and reinforcement learning. |
Do GANS (Generative Adversarial Nets) actually learn the target distribution? The foundational paper of Goodfellow et al. (2014) suggested they do, if they were given sufficiently large deep nets, sample size, and computation time. A recent theoretical analysis in Arora et al. (2017) raised doubts whether the same hold... | We propose a support size estimator of GANs's learned distribution to show they indeed suffer from mode collapse, and we prove that encoder-decoder GANs do not avoid the issue as well. |
Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of th... | We propose a framework to generate “natural” adversaries against black-box classifiers for both visual and textual domains, by doing the search for adversaries in the latent semantic space. |
Kronecker-factor Approximate Curvature (Martens & Grosse, 2015) (K-FAC) is a 2nd-order optimization method which has been shown to give state-of-the-art performance on large-scale neural network optimization tasks (Ba et al., 2017). It is based on an approximation to the Fisher information matrix (FIM) that makes ass... | We extend the K-FAC method to RNNs by developing a new family of Fisher approximations. |
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploi... | The generative model for kernels of convolutional neural networks, that acts as a prior distribution while training on new datasets. |
The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements. Motivated by the potential for an in-the-field cell sorting detector, we examine a Synechocystis sp. PCC 6803 dataset wherein cells are grown alternatively in nitrogen rich or deplete cultures. We u... | We applied deep learning techniques to hyperspectral image segmentation and iterative feature sampling. |
Data Interpretation is an important part of Quantitative Aptitude exams and requires an individual to answer questions grounded in plots such as bar charts, line graphs, scatter plots, \textit{etc}. Recently, there has been an increasing interest in building models which can perform this task by learning from datasets ... | We created a new dataset for data interpretation over plots and also propose a baseline for the same. |
Learning to predict complex time-series data is a fundamental challenge in a range of disciplines including Machine Learning, Robotics, and Natural Language Processing. Predictive State Recurrent Neural Networks (PSRNNs) (Downey et al.) are a state-of-the-art approach for modeling time-series data which combine the ben... | Improving Predictive State Recurrent Neural Networks via Orthogonal Random Features |
Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental q... | We propose Fidelity-weighted Learning, a semi-supervised teacher-student approach for training neural networks using weakly-labeled data. |
Online learning has attracted great attention due to the increasing demand for systems that have the ability of learning and evolving. When the data to be processed is also high dimensional and dimension reduction is necessary for visualization or prediction enhancement, online dimension reduction will play an essenti... | We proposed two new approaches, the incremental sliced inverse regression and incremental overlapping sliced inverse regression, to implement supervised dimension reduction in an online learning manner. |
This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Bytes. The main idea of the proposed model is to recursively recycle data storage of weights (parameters) during trainin... | We propose a learning model enabling DNN to learn with only 2 bit/weight, which is especially useful for on-device learning |
Within-class variation in a high-dimensional dataset can be modeled as being on a low-dimensional manifold due to the constraints of the physical processes producing that variation (e.g., translation, illumination, etc.). We desire a method for learning a representation of the manifolds induced by identity-preserving t... | Learning transport operators on manifolds forms a valuable representation for doing tasks like transfer learning. |
The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such r... | We present a neural variational model for learning language-guided compositional visual concepts. |
Despite much success in many large-scale language tasks, sequence-to-sequence (seq2seq) models have not been an ideal choice for conversational modeling as they tend to generate generic and repetitive responses. In this paper, we propose a Latent Topic Conversational Model (LTCM) that augments the seq2seq model with a ... | Latent Topic Conversational Model, a hybrid of seq2seq and neural topic model to generate more diverse and interesting responses. |
Most of the existing Graph Neural Networks (GNNs) are the mere extension of the Convolutional Neural Networks (CNNs) to graphs. Generally, they consist of several steps of message passing between the nodes followed by a global indiscriminate feature pooling function. In many data-sets, however, the nodes are unlabeled ... | The graph analysis problem is transformed into a point cloud analysis problem. |
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results.
Different attack strategies have been proposed to generate adversarial examples, but how ... | We propose to generate adversarial example based on generative adversarial networks in a semi-whitebox and black-box settings. |
This paper proposes a new model for the rating prediction task in recommender systems which significantly outperforms previous state-of-the art models on a time-split Netflix data set. Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training. We empirically demo... | This paper demonstrates how to train deep autoencoders end-to-end to achieve SoA results on time-split Netflix data set. |
Recurrent neural networks (RNNs) sequentially process data by updating their state with each new data point, and have long been the de facto choice for sequence modeling tasks. However, their inherently sequential computation makes them slow to train. Feed-forward and convolutional architectures have recently been show... | We introduce the Universal Transformer, a self-attentive parallel-in-time recurrent sequence model that outperforms Transformers and LSTMs on a wide range of sequence-to-sequence tasks, including machine translation. |
We present a framework for interpretable continual learning (ICL). We show that explanations of previously performed tasks can be used to improve performance on future tasks. ICL generates a good explanation of a finished task, then uses this to focus attention on what is important when facing a new task. The ICL idea ... | The paper develops an interpretable continual learning framework where explanations of the finished tasks are used to enhance the attention of the learner during the future tasks, and where an explanation metric is proposed too. |
The state-of-the-art (SOTA) for mixed precision training is dominated by variants of low precision floating point operations, and in particular, FP16 accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a lot of research has also happened in the domain of low and mixed-precision Integer training,... | Mixed precision training pipeline using 16-bit integers on general purpose HW; SOTA accuracy for ImageNet-class CNNs; Best reported accuracy for ImageNet-1K classification task with any reduced precision training; |
In this paper we introduce a new speech recognition system, leveraging a simple letter-based ConvNet acoustic model. The acoustic model requires only audio transcription for training -- no alignment annotations, nor any forced alignment step is needed. At inference, our decoder takes only a word list and a language mod... | A letter-based ConvNet acoustic model leads to a simple and competitive speech recognition pipeline. |
Generative adversarial networks (GANs) are a powerful framework for generative tasks. However, they are difficult to train and tend to miss modes of the true data generation process. Although GANs can learn a rich representation of the covered modes of the data in their latent space, the framework misses an inverse map... | A noval GAN framework that utilizes transformation-invariant features to learn rich representations and strong generators. |
We propose a method for learning the dependency structure between latent variables in deep latent variable models. Our general modeling and inference framework combines the complementary strengths of deep generative models and probabilistic graphical models. In particular, we express the latent variable space of a va... | We propose a method for learning latent dependency structure in variational autoencoders. |
Many real-world time series, such as in activity recognition, finance, or climate science, have changepoints where the system's structure or parameters change. Detecting changes is important as they may indicate critical events. However, existing methods for changepoint detection face challenges when (1) the patterns o... | We introduce a scale-invariant neural network architecture for changepoint detection in multivariate time series. |
We demonstrate how to learn efficient heuristics for automated reasoning algorithms through deep reinforcement learning. We focus on backtracking search algorithms for quantified Boolean logics, which already can solve formulas of impressive size - up to 100s of thousands of variables. The main challenge is to find a r... | RL finds better heuristics for automated reasoning algorithms. |
We consider the question of how to assess generative adversarial networks, in particular with respect to whether or not they generalise beyond memorising the training data. We propose a simple procedure for assessing generative adversarial network performance based on a principled consideration of what the actual goal ... | Assess whether or not your GAN is actually doing something other than memorizing the training data. |
The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various hand-designed alternatives to ReLU have been proposed, none have managed to... | We use search techniques to discover novel activation functions, and our best discovered activation function, f(x) = x * sigmoid(beta * x), outperforms ReLU on a number of challenging tasks like ImageNet. |
Successful training of convolutional neural networks is often associated with suffi-
ciently deep architectures composed of high amounts of features. These networks
typically rely on a variety of regularization and pruning techniques to converge
to less redundant states. We introduce a novel bottom-up approach to ex... | A bottom-up algorithm that expands CNNs starting with one feature per layer to architectures with sufficient representational capacity. |
Deep neural networks are almost universally trained with reverse-mode automatic differentiation (a.k.a. backpropagation). Biological networks, on the other hand, appear to lack any mechanism for sending gradients back to their input neurons, and thus cannot be learning in this way. In response to this, Scellier & Bengi... | We train a feedforward network without backprop by using an energy-based model to provide local targets |
We propose a novel generative model architecture designed to learn representations for images that factor out a single attribute from the rest of the representation. A single object may have many attributes which when altered do not change the identity of the object itself. Consider the human face; the identity of a pa... | Learn representations for images that factor out a single attribute. |
Stochastic video prediction models take in a sequence of image frames, and generate a sequence of consecutive future image frames. These models typically generate future frames in an autoregressive fashion, which is slow and requires the input and output frames to be consecutive. We introduce a model that overcomes the... | We present a model for consistent 3D reconstruction and jumpy video prediction e.g. producing image frames multiple time-steps in the future without generating intermediate frames. |
The ADAM optimizer is exceedingly popular in the deep learning community. Often it works very well, sometimes it doesn’t. Why? We interpret ADAM as a combination of two aspects: for each weight, the update direction is determined by the sign of the stochastic gradient, whereas the update magnitude is solely determined ... | Analyzing the popular Adam optimizer |
We propose a novel framework to adaptively adjust the dropout rates for the deep neural network based on a Rademacher complexity bound. The state-of-the-art deep learning algorithms impose dropout strategy to prevent feature co-adaptation. However, choosing the dropout rates remains an art of heuristics or relies on em... | We propose a novel framework to adaptively adjust the dropout rates for the deep neural network based on a Rademacher complexity bound. |
Sensor fusion is a key technology that integrates various sensory inputs to allow for robust decision making in many applications such as autonomous driving and robot control. Deep neural networks have been adopted for sensor fusion in a body of recent studies. Among these, the so-called netgated architecture was propo... | Optimized gated deep learning architectures for sensor fusion is proposed. |
We develop a mean field theory for batch normalization in fully-connected feedforward neural networks. In so doing, we provide a precise characterization of signal propagation and gradient backpropagation in wide batch-normalized networks at initialization. Our theory shows that gradient signals grow exponentially in d... | Batch normalization causes exploding gradients in vanilla feedforward networks. |
We present NeuroSAT, a message passing neural network that learns to solve SAT problems after only being trained as a classifier to predict satisfiability. Although it is not competitive with state-of-the-art SAT solvers, NeuroSAT can solve problems that are substantially larger and more difficult than it ever saw du... | We train a graph network to predict boolean satisfiability and show that it learns to search for solutions, and that the solutions it finds can be decoded from its activations. |
Spatiotemporal forecasting has various applications in neuroscience, climate and transportation domain. Traffic forecasting is one canonical example of such learning task. The task is challenging due to (1) complex spatial dependency on road networks, (2) non-linear temporal dynamics with changing road conditions and (... | A neural sequence model that learns to forecast on a directed graph. |
Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution, but it remains open how to specify their prior. In particular, the common practice of a standard normal prior in weight space imposes only weak regularities, ca... | We train neural networks to be uncertain on noisy inputs to avoid overconfident predictions outside of the training distribution. |
Convolutional neural networks (CNNs) were inspired by human vision and, in some settings, achieve a performance comparable to human object recognition. This has lead to the speculation that both systems use similar mechanisms to perform recognition. In this study, we conducted a series of simulations that indicate that... | This study highlights a key difference between human vision and CNNs: while object recognition in humans relies on analysing shape, CNNs do not have such a shape-bias. |
The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial neural networks. Different variants have been proposed where the underlying latent space is structured, for example, based on attributes descr... | We describe a novel multi-view generative model that can generate multiple views of the same object, or multiple objects in the same view with no need of label on views. |
The huge size of deep networks hinders their use in small computing devices. In this paper, we consider compressing the network by weight quantization. We extend a recently proposed loss-aware weight binarization scheme to ternarization, with possibly different scaling parameters for the positive and negative weights, ... | A loss-aware weight quantization algorithm that directly considers its effect on the loss is proposed. |
In the pursuit of increasingly intelligent learning systems, abstraction plays a vital role in enabling sophisticated decisions to be made in complex environments. The options framework provides formalism for such abstraction over sequences of decisions. However most models require that options be given a priori, pre... | We develop a novel policy gradient method for the automatic learning of policies with options using a differentiable inference step. |
The paper, interested in unsupervised feature selection, aims to retain the features best accounting for the local patterns in the data. The proposed approach, called Locally Linear Unsupervised Feature Selection, relies on a dimensionality reduction method to characterize such patterns; each feature is thereafter asse... | Unsupervised feature selection through capturing the local linear structure of the data |
Humans can understand and produce new utterances effortlessly, thanks to their systematic compositional skills. Once a person learns the meaning of a new verb "dax," he or she can immediately understand the meaning of "dax twice" or "sing and dax." In this paper, we introduce the SCAN domain, consisting of a set of sim... | Using a simple language-driven navigation task, we study the compositional capabilities of modern seq2seq recurrent networks. |
This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks (GNN), which have emerged as an effective model for various supervised prediction problems defined on structured data, can be trained to p... | We tackle the problem of similarity learning for structured objects with applications in particular in computer security, and propose a new model graph matching networks that excels on this task. |
Context information plays an important role in human language understanding, and it is also useful for machines to learn vector representations of language. In this paper, we explore an asymmetric encoder-decoder structure for unsupervised context-based sentence representation learning. As a result, we build an encoder... | We proposed an RNN-CNN encoder-decoder model for fast unsupervised sentence representation learning. |
Building on the success of deep learning, two modern approaches to learn a probability model of the observed data are Generative Adversarial Networks (GANs) and Variational AutoEncoders (VAEs). VAEs consider an explicit probability model for the data and compute a generative distribution by maximizing a variational low... | A statistical approach to compute sample likelihoods in Generative Adversarial Networks |
We introduce geomstats, a Python package for Riemannian modelization and optimization over manifolds such as hyperspheres, hyperbolic spaces, SPD matrices or Lie groups of transformations. Our contribution is threefold. First, geomstats allows the flexible modeling of many a machine learning problem through an efficien... | We introduce geomstats, an efficient Python package for Riemannian modelization and optimization over manifolds compatible with both numpy and tensorflow . |
We propose to execute deep neural networks (DNNs) with dynamic and sparse graph (DSG) structure for compressive memory and accelerative execution during both training and inference. The great success of DNNs motivates the pursuing of lightweight models for the deployment onto embedded devices. However, most of the prev... | We construct dynamic sparse graph via dimension-reduction search to reduce compute and memory cost in both DNN training and inference. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.