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In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level. We propose a systematic pipeline for interpreting individual hidden state dynamics within the network using response characterization methods. The ranked ... | Introducing the response charactrization method for interpreting cell dynamics in learned long short-term memory (LSTM) networks. |
Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns. However, the mechanisms and fu... | To our knowledge, this is the first study to show how neural representations of space, including grid-like cells and border cells as observed in the brain, could emerge from training a recurrent neural network to perform navigation tasks. |
Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song. Such components include voice, bass, drums and any other accompaniments. While end-to-end models that directly generate the waveform are state-of-the-art i... | We match the performance of spectrogram based model with a model trained end-to-end in the waveform domain |
Although challenging, strategy profile evaluation in large connected learner networks is crucial for enabling the next wave of machine learning applications. Recently, $\alpha$-Rank, an evolutionary algorithm, has been proposed as a solution for ranking joint policy profiles in multi-agent systems. $\alpha$-Rank claime... | We provide a scalable solution to multi-agent evaluation with linear rate complexity in both time and memory in terms of number of agents |
Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data. In this work, we propose DivideMix, a novel ... | We propose a novel semi-supervised learning approach with SOTA performance on combating learning with noisy labels. |
We present a new algorithm to train a robust neural network against adversarial attacks.
Our algorithm is motivated by the following two ideas. First, although recent work has demonstrated that fusing randomness can improve the robustness of neural networks (Liu 2017), we noticed that adding noise blindly to all the ... | We design an adversarial training method to Bayesian neural networks, showing a much stronger defense to white-box adversarial attacks |
Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server. In this work, we explore the threat of model poisoning attacks on federated learning initia... | Effective model poisoning attacks on federated learning able to cause high-confidence targeted misclassification of desired inputs |
Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs. Small amounts of noise can destroy the performance of an otherwise state-of-the-art model. To harden models against background noise, practitioners often perform data augmentation, adding artificial... | In this paper, we hypothesize that superficially perturbed data points shouldn’t merely map to the same class---they should map to the same representation. |
In this paper we design a harmonic acoustic model for pitch detection. This model arranges conventional convolution and sparse convolution in a way such that the global harmonic patterns captured by sparse convolution are composed of the enough number of local patterns captured by layers of conventional convolution. Wh... | harmonic acoustic model |
Learning domain-invariant representation is a dominant approach for domain generalization. However, previous methods based on domain invariance overlooked the underlying dependency of classes on domains, which is responsible for the trade-off between classification accuracy and the invariance. This study proposes a nov... | Address the trade-off caused by the dependency of classes on domains by improving domain adversarial nets |
Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images. Following their successful application in image processing and representation learning, an important next step is to consider videos. Learning generative models of video is a much harder task, requiring a mod... | We propose FVD: a new metric for generative models of video based on FID. A large-scale human study confirms that FVD correlates well with qualitative human judgment of generated videos. |
Despite advances in deep learning, artificial neural networks do not learn the same way as humans do. Today, neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on learnt tasks when tasks are presented one at a time -- this phenomenon called catastrophic forgetting is ... | A dual memory architecture inspired from human brain to learn sequentially incoming tasks, while averting catastrophic forgetting. |
Most research on lifelong learning applies to images or games, but not language.
We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling.
LAMOL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity.
Specifically, LAMOL is a ... | Language modeling for lifelong language learning. |
Deep learning natural language processing models often use vector word embeddings, such as word2vec or GloVe, to represent words. A discrete sequence of words can be much more easily integrated with downstream neural layers if it is represented as a sequence of continuous vectors. Also, semantic relationships between ... | We use ideas from quantum computing to proposed word embeddings that utilize much fewer trainable parameters. |
One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary. Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.... | We train neural network agents to develop a language with compositional properties from raw pixel input. |
The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles). In particular, a set of possible future behaviors generated by the system must be diverse to account for ... | We learn a diversity sampling function with DPPs to obtain a diverse set of samples from a generative model. |
There is mounting evidence that pretraining can be valuable for neural network language understanding models, but we do not yet have a clear understanding of how the choice of pretraining objective affects the type of linguistic information that models learn. With this in mind, we compare four objectives---language mod... | Representations from language models consistently perform better than translation encoders on syntactic auxiliary prediction tasks. |
We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied. Consider, for example, the trade-off between thermal resistance, electrical conductivity, and mechanic... | We propose surrogate based Constrained Langevin sampling with application in nano-porous material configuration design. |
There is growing interest in geometrically-inspired embeddings for learning hierarchies, partial orders, and lattice structures, with natural applications to transitive relational data such as entailment graphs. Recent work has extended these ideas beyond deterministic hierarchies to probabilistically calibrated models... | Improve hierarchical embedding models using kernel smoothing |
We present a weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities (e.g., proteins) from the scientific literature. First, we train a neural NER (NNER) model over a small seed of fully-labeled examples. Second, we use a reference s... | Augmented bootstrapping approach combining information from a reference set with iterative refinements of soft labels to improve Name Entity Recognition from biomedical literature. |
Quantum machine learning methods have the potential to facilitate learning using extremely large datasets. While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels. One of the approaches fo... | We extend quantum SVMs to semi-supervised setting, to deal with the likely problem of many missing class labels in huge datasets. |
Deep neural networks have become the state-of-the-art models in numerous machine learning tasks. However, general guidance to network architecture design is still missing. In our work, we bridge deep neural network design with numerical differential equations. We show that many effective networks, such as ResNet, PolyN... | This paper bridges deep network architectures with numerical (stochastic) differential equations. This new perspective enables new designs of more effective deep neural networks. |
Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications. In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically... | CNN and LSTM to generate markup-like code describing graphical user interface images. |
Computer vision tasks such as image classification, image retrieval and few-shot learning are currently dominated by Euclidean and spherical embeddings, so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances ... | We show that hyperbolic embeddings are useful for high-level computer vision tasks, especially for few-shot classification. |
High-dimensional time series are common in many domains. Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations. However, most representation learning algorithms for time series data are difficult to interpret. This is... | We present a method to learn interpretable representations on time series using ideas from variational autoencoders, self-organizing maps and probabilistic models. |
We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting syste... | Convolutional architecture for learning data-dependent weights for autoregressive forecasting of time series. |
MixUp is a data augmentation scheme in which pairs of training samples and their corresponding labels are mixed using linear coefficients. Without label mixing, MixUp becomes a more conventional scheme: input samples are moved but their original labels are retained. Because samples are preferentially moved in the direc... | We present a novel interpretation of MixUp as belonging to a class highly analogous to adversarial training, and on this basis we introduce a simple generalization which outperforms MixUp |
Plan recognition aims to look for target plans to best explain the observed actions based on plan libraries and/or domain models. Despite the success of previous approaches on plan recognition, they mostly rely on correct action observations.
Recent advances in visual activity recognition have the potential of enabli... | Handling Uncertainty in Visual Perception for Plan Recognition |
We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB). In particular, we describe a neural module, DrKIT, that traverses textual data like a virtual KB, softly following paths of relations between mentions of entities in the corpus. At each step the operation uses... | Differentiable multi-hop access to a textual knowledge base of indexed contextual representations |
In spite of their great success, traditional factorization algorithms typically do not support features (e.g., Matrix Factorization), or their complexity scales quadratically with the number of features (e.g, Factorization Machine). On the other hand, neural methods allow large feature sets, but are often designed for ... | Scalable general-purpose factorization algorithm-- also helps to circumvent cold start problem. |
Augmented Reality (AR) can assist with physical tasks such as object assembly through the use of “situated instructions”. These instructions can be in the form of videos, pictures, text or guiding animations, where the most helpful media among these is highly dependent on both the user and the nature of the task. Our w... | We present a mixed media assembly tutorial authoring system that streamlines creation of videos, images, text and dynamic instructions in situ. |
Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on... | We demostarte that using clinical notes in conjuntion with ICU instruments data improves the perfomance on ICU management benchmark tasks |
Existing works in deep Multi-Agent Reinforcement Learning (MARL) mainly focus on coordinating cooperative agents to complete certain tasks jointly. However, in many cases of the real world, agents are self-interested such as employees in a company and clubs in a league. Therefore, the leader, i.e., the manager of the c... | We propose an event-based policy gradient to train the leader and an action abstraction policy gradient to train the followers in leader-follower Markov game. |
Recent work has studied the emergence of language among deep reinforcement learning agents that must collaborate to solve a task. Of particular interest are the factors that cause language to be compositional---i.e., express meaning by combining words which themselves have meaning. Evolutionary linguists have found tha... | We use cultural transmission to encourage compositionality in languages that emerge from interactions between neural agents. |
Based on our observation that there exists a dramatic drop for the singular values of the fully connected layers or a single feature map of the convolutional layer, and that the dimension of the concatenated feature vector almost equals the summation of the dimension on each feature map, we propose a singular value dec... | We propose a SVD based method to explore the local dimension of activation manifold in deep neural networks. |
Large pre-trained Transformers such as BERT have been tremendously effective for many NLP tasks. However, inference in these large-capacity models is prohibitively slow and expensive . Transformers are essentially a stack of self-attention layers which encode each input position using the entire input sequence as its ... | Inference in large Transformers is expensive due to the self-attention in multiple layers. We show a simple decomposition technique can yield a faster, low memory-footprint model that is just as accurate of the original models. |
Exploration while learning representations is one of the main challenges Deep
Reinforcement Learning (DRL) faces today. As the learned representation is dependant in the observed data, the exploration strategy has a crucial role. The popular DQN algorithm has improved significantly the capabilities of Reinforcement
L... | A Deep Learning adaptation of Randomized Least Squares Value Iteration |
The complexity of large-scale neural networks can lead to poor understanding of their internal details. We show that this opaqueness provides an opportunity for adversaries to embed unintended functionalities into the network in the form of Trojan horse attacks. Our novel framework hides the existence of a malicious n... | Parameters of a trained neural network can be permuted to produce a completely separate model for a different task, enabling the embedding of Trojan horse networks inside another network. |
In this paper, we introduce Random Path Generative Adversarial Network (RPGAN) --- an alternative scheme of GANs that can serve as a tool for generative model analysis. While the latent space of a typical GAN consists of input vectors, randomly sampled from the standard Gaussian distribution, the latent space of RPGAN ... | We introduce an alternative GAN design based on random routes in generator, which can serve as a tool for generative models interpretability. |
Deep artificial neural networks can achieve an extremely small difference between training and test accuracies on identically distributed training and test sets, which is a standard measure of generalization. However, the training and test sets may not be sufficiently representative of the empirical sample set, which c... | We present a theoretical and experimental framework for defining, understanding, and achieving generalization, and as a result robustness, in deep learning by drawing on algorithmic information theory and coding theory. |
Many approaches to causal discovery are limited by their inability to discriminate between Markov equivalent graphs given only observational data. We formulate causal discovery as a marginal likelihood based Bayesian model selection problem. We adopt a parameterization based on the notion of the independence of causal ... | We cast causal structure discovery as a Bayesian model selection in a way that allows us to discriminate between Markov equivalent graphs to identify the unique causal graph. |
The goal of compressed sensing is to learn a structured signal $x$
from a limited number of noisy linear measurements $y \approx Ax$. In
traditional compressed sensing, ``structure'' is represented by
sparsity in some known basis. Inspired by the success of deep
learning in modeling images, recent work sta... | Lower bound for compressed sensing w/ generative models that matches known upper bounds |
We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn ‘distributional similarity’ in a multimodal feature space, by mapping a test image to similar training images in this space and generating a caption from the same space. To validate our hypothesis, we focus... | This paper presents an empirical analysis on the role of different types of image representations and probes the properties of these representations for the task of image captioning. |
We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory. To account for the fact that typically there are multiple correct SQL queries with the same or very similar semantics, we draw inspi... | We design incremental sequence-to-action parsers for text-to-SQL task and achieve SOTA results. We further improve by using non-deterministic oracles to allow multiple correct action sequences. |
We propose Efficient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods. In ENAS, a controller learns to discover neural network architectures by searching for an optimal path within a larger model. The controller is trained with policy gradient to se... | An approach that speeds up neural architecture search by 10x, whilst using 100x less computing resource. |
Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech. Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable. In particular, there is ... | We propose a new DNN architecture for deep learning on tabular data |
Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one. State-of-the-art unsupervised domain adaptation methods for person ... | A framework that conducts online refinement of pseudo labels with a novel soft softmax-triplet loss for unsupervised domain adaptation on person re-identification. |
We present the first end-to-end verifier of audio classifiers. Compared to existing methods, our approach enables analysis of both, the entire audio processing stage as well as recurrent neural network architectures (e.g., LSTM). The audio processing is verified using novel convex relaxations tailored to feature extrac... | We present the first approach to certify robustness of neural networks against noise-based perturbations in the audio domain. |
Since deep neural networks are over-parameterized, they can memorize noisy examples. We address such memorizing issue in the presence of annotation noise. From the fact that deep neural networks cannot generalize neighborhoods of the features acquired via memorization, we hypothesize that noisy examples do not consiste... | This work presents a method of generating and using ensembles effectively to identify noisy examples in the presence of annotation noise. |
Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications. A popular formulation of the problem is an $\ell_1$ regularized maximum likelihood estimation. Many convex optimization algorithms have been designed to solve this formulation to recover the ... | A data-driven learning algorithm based on unrolling the Alternating Minimization optimization for sparse graph recovery. |
Counterfactual regret minimization (CFR) is a fundamental and effective technique for solving Imperfect Information Games (IIG). However, the original CFR algorithm only works for discrete states and action spaces, and the resulting strategy is maintained as a tabular representation. Such tabular representation limits ... | We proposed a double neural framework to solve large-scale imperfect information game. |
We present the first verification that a neural network for perception tasks produces
a correct output within a specified tolerance for every input of interest. We define
correctness relative to a specification which identifies 1) a state space consisting of
all relevant states of the world and 2) an observation pro... | We present the first verification that a neural network for perception tasks produces a correct output within a specified tolerance for every input of interest. |
Deep generative models have achieved remarkable progress in recent years. Despite this progress, quantitative evaluation and comparison of generative models remains as one of the important challenges. One of the most popular metrics for evaluating generative models is the log-likelihood. While the direct computation of... | We study rate distortion approximations for evaluating deep generative models, and show that rate distortion curves provide more insights about the model than the log-likelihood alone while requiring roughly the same computational cost. |
Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time. Given that it is impractical ... | A model-based meta-RL algorithm that enables a real robot to adapt online in dynamic environments |
Model-free deep reinforcement learning approaches have shown superhuman performance in simulated environments (e.g., Atari games, Go, etc). During training, these approaches often implicitly construct a latent space that contains key information for decision making. In this paper, we learn a forward model on this laten... | The paper analyzes the latent space learned by model-free approaches in a miniature incomplete information game, trains a forward model in the latent space and apply it to Monte-Carlo Tree Search, yielding positive performance. |
Several state of the art convolutional networks rely on inter-connecting different layers to ease the flow of information and gradient between their input and output layers. These techniques have enabled practitioners to successfully train deep convolutional networks with hundreds of layers. Particularly, a novel way o... | We analyze the expressive power of the connections used in DenseNets via tensor decompositions. |
We consider the following central question in the field of Deep Reinforcement Learning (DRL): How can we use implicit human feedback to accelerate and optimize the training of a DRL algorithm? State-of-the-art methods rely on any human feedback to be provided explicitly, requiring the active participation of humans (e.... | We use implicit human feedback (via error-potentials, EEG) to accelerate and optimize the training of a DRL algorithm, in a practical manner. |
Deep learning has demonstrated abilities to learn complex structures, but they can be restricted by available data. Recently, Consensus Networks (CNs) were proposed to alleviate data sparsity by utilizing features from multiple modalities, but they too have been limited by the size of labeled data. In this paper, we ex... | TCN for multimodal semi-supervised learning + ablation study of its mechanisms + interpretations of latent representations |
Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings. However, some of the most popular of these optimization tools - namely A... | Adapting Adam, Amsgrad, Adagrad to Riemannian manifolds. |
We study the problem of defending deep neural network approaches for image classification from physically realizable attacks. First, we demonstrate that the two most scalable and effective methods for learning robust models, adversarial training with PGD attacks and randomized smoothing, exhibit very limited effectiven... | Defending Against Physically Realizable Attacks on Image Classification |
Continual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge. However, catastrophic forgetting poses a grand challenge for neural networks performing such learning process. Thus, neural networks that are deployed in the real world often struggle in sce... | Hebbian plastic weights can behave as a compressed episodic memory storage in neural networks and with the combination of task-specific synaptic consolidation can improve the ability to alleviate catastrophic forgetting in continual learning. |
In order to choose a neural network architecture that will be effective for a particular modeling problem, one must understand the limitations imposed by each of the potential options. These limitations are typically described in terms of information theoretic bounds, or by comparing the relative complexity needed to a... | This paper proves that skinny neural networks cannot approximate certain functions, no matter how deep they are. |
Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs. Inferring loop invariants is one of the main challenges behind automated verification of real-world programs which often contain many loops. In this paper, we present Continuous L... | We introduce the Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants and general SMT formulas. |
Single cell RNA sequencing (scRNAseq) technology enables quantifying gene expression profiles by individual cells within cancer. Dimension reduction methods have been commonly used for cell clustering analysis and visualization of the data. Current dimension reduction methods tend overly eliminate the expression variat... | Our finding shed lights in preventing cancer progression |
While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown. In this paper, we show that flows can in fact be extended to discrete events---and under a simple change-of-variables formula not requiring log-d... | We extend autoregressive flows and RealNVP to discrete data. |
We present a Deep Neural Network with Spike Assisted Feature Extraction (SAFE-DNN) to improve robustness of classification under stochastic perturbation of inputs. The proposed network augments a DNN with unsupervised learning of low-level features using spiking neuron network (SNN) with Spike-Time-Dependent-Plasticity... | A noise robust deep learning architecture. |
Neural embeddings have been used with great success in Natural Language Processing (NLP) where they provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks. The success of neural embeddings has prompted significant amounts of research into ... | We learn neural embeddings of graphs in hyperbolic instead of Euclidean space |
The International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS) plays a pivotal role in fostering the development of new Knowledge Engineering (KE) tools, and in emphasising the importance of principled approaches for all the different KE aspects that are needed for the successful long-term ... | Ideas for future ICKEPS |
We show that generating English Wikipedia articles can be approached as a multi-
document summarization of source documents. We use extractive summarization
to coarsely identify salient information and a neural abstractive model to generate
the article. For the abstractive model, we introduce a decoder-only architec... | We generate Wikipedia articles abstractively conditioned on source document text. |
Abstract Stochastic gradient descent (SGD) and Adam are commonly used to optimize deep neural networks, but choosing one usually means making tradeoffs between speed, accuracy and stability. Here we present an intuition for why the tradeoffs exist as well as a method for unifying the two in a continuous way. This makes... | An algorithm for unifying SGD and Adam and empirical study of its performance |
The use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging. In fact, previous work has shown that when the modes are known, learning separate policies for each mode or sub-task can greatly improve the performance of imitation learning. ... | Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information |
The Convolutional Neural Network (CNN) has been successfully applied in many fields during recent decades; however it lacks the ability to utilize prior domain knowledge when dealing with many realistic problems. We present a framework called Geometric Operator Convolutional Neural Network (GO-CNN) that uses domain kno... | Traditional image processing algorithms are combined with Convolutional Neural Networks，a new neural network. |
Determinantal point processes (DPPs) is an effective tool to deliver diversity on multiple machine learning and computer vision tasks. Under deep learning framework, DPP is typically optimized via approximation, which is not straightforward and has some conflict with diversity requirement. We note, however, there has b... | We proposed a specific back-propagation method via proper spectral sub-gradient to integrate determinantal point process to deep learning framework. |
The quality of a machine translation system depends largely on the availability of sizable parallel corpora. For the recently popular Neural Machine Translation (NMT) framework, data sparsity problem can become even more severe. With large amount of tunable parameters, the NMT model may overfit to the existing language... | We invent a novel cluster-to-cluster framework for NMT training, which can better understand the both source and target language diversity. |
The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain the problem in the scope of inductive logic programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an efficient differenti... | An efficient differentiable ILP model that learns first-order logic rules that can explain the data. |
Neural network-based classifiers parallel or exceed human-level accuracy on many common tasks and are used in practical systems. Yet, neural networks are susceptible to adversarial examples, carefully perturbed inputs that cause networks to misbehave in arbitrarily chosen ways. When generated with standard methods, the... | We introduce a new method for synthesizing adversarial examples robust in the physical world and use it to fabricate the first 3D adversarial objects. |
Representations of sets are challenging to learn because operations on sets should be permutation-invariant. To this end, we propose a Permutation-Optimisation module that learns how to permute a set end-to-end. The permuted set can be further processed to learn a permutation-invariant representation of that set, avoid... | Learn how to permute a set, then encode permuted set with RNN to obtain a set representation. |
The physical design of a robot and the policy that controls its motion are inherently coupled. However, existing approaches largely ignore this coupling, instead choosing to alternate between separate design and control phases, which requires expert intuition throughout and risks convergence to suboptimal designs. In t... | Use deep reinforcement learning to design the physical attributes of a robot jointly with a control policy. |
Deep learning enables training of large and flexible function approximators from scratch at the cost of large amounts of data. Applications of neural networks often consider learning in the context of a single task. However, in many scenarios what we hope to learn is not just a single task, but a model that can be used... | We propose an approach that endows a single model with the ability to represent both extremes: joint training and independent training, which leads to effective multi-task learning. |
Training agents to operate in one environment often yields overfitted models that are unable to generalize to the changes in that environment. However, due to the numerous variations that can occur in the real-world, the agent is often required to be robust in order to be useful. This has not been the case for agents t... | We propose a regularization term that, when added to the reinforcement learning objective, allows the policy to maximize the reward and simultaneously learn to be invariant to the irrelevant changes within the input.. |
Though visual information has been introduced for enhancing neural machine translation (NMT), its effectiveness strongly relies on the availability of large amounts of bilingual parallel sentence pairs with manual image annotations. In this paper, we present a universal visual representation learned over the monolingua... | This work proposed a universal visual representation for neural machine translation (NMT) using retrieved images with similar topics to source sentence, extending image applicability in NMT. |
This paper introduces a novel framework for learning algorithms to solve online combinatorial optimization problems. Towards this goal, we introduce a number of key ideas from traditional algorithms and complexity theory. First, we draw a new connection between primal-dual methods and reinforcement learning. Next, we i... | By combining ideas from traditional algorithms design and reinforcement learning, we introduce a novel framework for learning algorithms that solve online combinatorial optimization problems. |
Despite their popularity and successes, deep neural networks are poorly understood theoretically and treated as 'black box' systems. Using a functional view of these networks gives us a useful new lens with which to understand them. This allows us us to theoretically or experimentally probe properties of these networks... | A functional approach reveals that flat initialization, preserved by gradient descent, leads to generalization ability. |
It is well-known that deeper neural networks are harder to train than shallower ones. In this short paper, we use the (full) eigenvalue spectrum of the Hessian to explore how the loss landscape changes as the network gets deeper, and as residual connections are added to the architecture. Computing a series of quantitat... | Network depth increases outlier eigenvalues in the Hessian. Residual connections mitigate this. |
In the context of optimization, a gradient of a neural network indicates the amount a specific weight should change with respect to the loss. Therefore, small gradients indicate a good value of the weight that requires no change and can be kept frozen during training. This paper provides an experimental study on the im... | An experimental paper that proves the amount of redundant weights that can be freezed from the third epoch only, with only a very slight drop in accuracy. |
Like humans, deep networks learn better when samples are organized and introduced in a meaningful order or curriculum. While conventional approaches to curriculum learning emphasize the difficulty of samples as the core incremental strategy, it forces networks to learn from small subsets of data while introducing pre-c... | A novel approach to curriculum learning by incrementally learning labels and adaptively smoothing labels for mis-classified samples which boost average performance and decreases standard deviation. |
Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the ``long tail'' of this distribution requires enormous amounts of data.
Representations of rare words trained directly on end tasks are usually poor, requiring us to pre-t... | We propose a method to deal with rare words by computing their embedding from definitions. |
The capability of reliably detecting out-of-distribution samples is one of the key factors in deploying a good classifier, as the test distribution always does not match with the training distribution in most real-world applications. In this work, we propose a deep generative classifier which is effective to detect out... | This paper proposes a deep generative classifier which is effective to detect out-of-distribution samples as well as classify in-distribution samples, by integrating the concept of Gaussian discriminant analysis into deep neural networks. |
One of the most prevalent symptoms among the elderly population, dementia, can be detected by classifiers trained on linguistic features extracted from narrative transcripts. However, these linguistic features are impacted in a similar but different fashion by the normal aging process. Aging is therefore a confounding ... | Show that age confounds cognitive impairment detection + solve with fair representation learning + propose metrics and models. |
Much of the focus in the design of deep neural networks had been on improving accuracy, leading to more powerful yet highly complex network architectures that are difficult to deploy in practical scenarios. As a result, there has been a recent interest in the design of quantitative metrics for evaluating deep neural ... | We introduce NetScore, new metric designed to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network. |
Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, especially white-box targeted attacks. This paper studies the problem of how aggressive white-box targeted attacks can be to go beyond widely used Top-1 attacks. We propose to learn ordered Top-k attacks (k>=1), which enforce the Top-k predicted labels ... | ordered Top-k adversarial attacks |
Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend. In this paper, we use the relationshi... | Personalized propagation of neural predictions (PPNP) improves graph neural networks by separating them into prediction and propagation via personalized PageRank. |
Most of recent work in cross-lingual word embeddings is severely Anglocentric. The vast majority of lexicon induction evaluation dictionaries are between English and another language, and the English embedding space is selected by default as the hub when learning in a multilingual setting. With this work, however, we c... | The choice of the hub (target) language affects the quality of cross-lingual embeddings, which shouldn't be evaluated only on English-centric dictionaries. |
Interpreting generative adversarial network (GAN) training as approximate divergence minimization has been theoretically insightful, has spurred discussion, and has lead to theoretically and practically interesting extensions such as f-GANs and Wasserstein GANs. For both classic GANs and f-GANs, there is an original va... | Typical GAN training doesn't optimize Jensen-Shannon, but something like a reverse KL divergence. |
REINFORCE can be used to train models in structured prediction settings to directly optimize the test-time objective. However, the common case of sampling one prediction per datapoint (input) is data-inefficient. We show that by drawing multiple samples (predictions) per datapoint, we can learn with significantly less ... | We show that by drawing multiple samples (predictions) per input (datapoint), we can learn with less data as we freely obtain a REINFORCE baseline. |
Reinforcement learning (RL) is a powerful technique to train an agent to perform a task. However, an agent that is trained using RL is only capable of achieving the single task that is specified via its reward function. Such an approach does not scale well to settings in which an agent needs to perform a diverse s... | We efficiently solve multi-task problems with an automatic curriculum generation algorithm based on a generative model that tracks the learning agent's performance. |
A wide range of defenses have been proposed to harden neural networks against adversarial attacks. However, a pattern has emerged in which the majority of adversarial defenses are quickly broken by new attacks. Given the lack of success at generating robust defenses, we are led to ask a fundamental question: Are adv... | This paper identifies classes of problems for which adversarial examples are inescapable, and derives fundamental bounds on the susceptibility of any classifier to adversarial examples. |
For computer vision applications, prior works have shown the efficacy of reducing numeric precision of model parameters (network weights) in deep neural networks. Activation maps, however, occupy a large memory footprint during both the training and inference step when using mini-batches of inputs. One way to reduce th... | Lowering precision (to 4-bits, 2-bits and even binary) and widening the filter banks gives as accurate network as those obtained with FP32 weights and activations. |
We investigate methods for semi-supervised learning (SSL) of a neural linear-chain conditional random field (CRF) for Named Entity Recognition (NER) by treating the tagger as the amortized variational posterior in a generative model of text given tags. We first illustrate how to incorporate a CRF in a VAE, enabling end... | We embed a CRF in a VAE of tokens and NER tags for semi-supervised learning and show improvements in low-resource settings. |
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. |
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