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Graph Neural Networks (GNNs) have received tremendous attention recently due to their power in handling graph data for different downstream tasks across different application domains. The key of GNN is its graph convolutional filters, and recently various kinds of filters are designed. However, there still lacks in-dep... | Propose an assessment framework to analyze and learn graph convolutional filter |
The advance of node pooling operations in Graph Neural Networks (GNNs) has lagged behind the feverish design of new message-passing techniques, and pooling remains an important and challenging endeavor for the design of deep architectures.
In this paper, we propose a pooling operation for GNNs that leverages a differe... | A new pooling layer for GNNs that learns how to pool nodes, according to their features, the graph connectivity, and the dowstream task objective. |
Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is the task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple yet powerful linear model based on Tucker decomposition of... | We propose TuckER, a relatively simple but powerful linear model for link prediction in knowledge graphs, based on Tucker decomposition of the binary tensor representation of knowledge graph triples. |
With innovations in architecture design, deeper and wider neural network models deliver improved performance on a diverse variety of tasks. But the increased memory footprint of these models presents a challenge during training, when all intermediate layer activations need to be stored for back-propagation. Limited GPU... | An algorithm to reduce the amount of memory required for training deep networks, based on an approximation strategy. |
Estimating the frequencies of elements in a data stream is a fundamental task in data analysis and machine learning. The problem is typically addressed using streaming algorithms which can process very large data using limited storage. Today's streaming algorithms, however, cannot exploit patterns in their input to imp... | Data stream algorithms can be improved using deep learning, while retaining performance guarantees. |
Link prediction in simple graphs is a fundamental problem in which new links between nodes are predicted based on the observed structure of the graph. However, in many real-world applications, there is a need to model relationships among nodes which go beyond pairwise associations. For example, in a chemical reaction, ... | We propose Neural Hyperlink Predictor (NHP). NHP adapts graph convolutional networks for link prediction in hypergraphs |
In this paper, we present a method for adversarial decomposition of text representation. This method can be used to decompose a representation of an input sentence into several independent vectors, where each vector is responsible for a specific aspect of the input sentence. We evaluate the proposed method on two case ... | A method which learns separate representations for the meaning and the form of a sentence |
We were approached by a group of healthcare providers who are involved in the care of chronic patients looking for potential technologies to facilitate the process of reviewing patient-generated data during clinical visits. Aiming at understanding the healthcare providers' attitudes towards reviewing patient-generate... | We explored the visualization designs that can support chronic patients to present and review their health data with healthcare providers during clinical visits. |
Recently, neural-network based forward dynamics models have been proposed that attempt to learn the dynamics of physical systems in a deterministic way. While near-term motion can be predicted accurately, long-term predictions suffer from accumulating input and prediction errors which can lead to pl... | We propose a stochastic differentiable forward dynamics predictor that is able to sample multiple physically plausible trajectories under the same initial input state and show that it can be used to train model-free policies more efficiently. |
There has been a large amount of interest, both in the past and particularly recently, into the relative advantage of different families of universal function approximators, for instance neural networks, polynomials, rational functions, etc. However, current research has focused almost exclusively on understanding this... | Beyond-worst-case analysis of the representational power of ReLU nets & polynomial kernels -- in particular in the presence of sparse latent structure. |
Recent deep generative models can provide photo-realistic images as well as visual or textual content embeddings useful to address various tasks of computer vision and natural language processing. Their usefulness is nevertheless often limited by the lack of control over the generative process or the poor understanding... | A model to control the generation of images with GAN and beta-VAE with regard to scale and position of the objects |
Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps. However, choosing which of the myriad structured transformations to use (and... | We propose a differentiable family of "kaleidoscope matrices," prove that all structured matrices can be represented in this form, and use them to replace hand-crafted linear maps in deep learning models. |
We propose a method, called Label Embedding Network, which can learn label representation (label embedding) during the training process of deep networks. With the proposed method, the label embedding is adaptively and automatically learned through back propagation. The original one-hot represented loss function is conv... | Learning Label Representation for Deep Networks |
Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters. As current approaches are limited by network bandwidth, we propose the use of communication compression in the decentralized trainin... | We propose Choco-SGD---decentralized SGD with compressed communication---for non-convex objectives and show its strong performance in various deep learning applications (on-device learning, datacenter case). |
We show that Entropy-SGD (Chaudhari et al., 2017), when viewed as a learning algorithm, optimizes a PAC-Bayes bound on the risk of a Gibbs (posterior) classifier, i.e., a randomized classifier obtained by a risk-sensitive perturbation of the weights of a learned classifier. Entropy-SGD works by optimizing the bound’s p... | We show that Entropy-SGD optimizes the prior of a PAC-Bayes bound, violating the requirement that the prior be independent of data; we use differential privacy to resolve this and improve generalization. |
In this paper, we investigate learning the deep neural networks for automated optical inspection in industrial manufacturing. Our preliminary result has shown the stunning performance improvement by transfer learning from the completely dissimilar source domain: ImageNet. Further study for demystifying this improvement... | We experimentally show that transfer learning makes sparse features in the network and thereby produces a more compressible network. |
The key challenge in semi-supervised learning is how to effectively leverage unlabeled data to improve learning performance. The classical label propagation method, despite its popularity, has limited modeling capability in that it only exploits graph information for making predictions. In this paper, we consider label... | We extend the classical label propation methods to jointly model graph and feature information from a graph filtering perspective, and show connections to the graph convlutional networks. |
Because the choice and tuning of the optimizer affects the speed, and ultimately the performance of deep learning, there is significant past and recent research in this area. Yet, perhaps surprisingly, there is no generally agreed-upon protocol for the quantitative and reproducible evaluation of optimization strategies... | We provide a software package that drastically simplifies, automates, and improves the evaluation of deep learning optimizers. |
Pre-trained word embeddings are the primary
method for transfer learning in several Natural Language Processing (NLP) tasks. Recent
works have focused on using unsupervised
techniques such as language modeling to obtain these embeddings. In contrast, this work
focuses on extracting representations from
multiple pr... | extract contextual embeddings from off-the-shelf supervised model. Helps downstream NLP models in low-resource settings |
We build a theoretical framework for understanding practical meta-learning methods that enables the integration of sophisticated formalizations of task-similarity with the extensive literature on online convex optimization and sequential prediction algorithms in order to provide within-task performance guarantees. Our ... | Practical adaptive algorithms for gradient-based meta-learning with provable guarantees. |
In this work, we propose a self-supervised method to learn sentence representations with an injection of linguistic knowledge. Multiple linguistic frameworks propose diverse sentence structures from which semantic meaning might be expressed out of compositional words operations. We aim to take advantage of this linguis... | We aim to exploit the diversity of linguistic structures to build sentence representations. |
The peripheral nervous system represents the input/output system for the brain. Cuff electrodes implanted on the peripheral nervous system allow observation and control over this system, however, the data produced by these electrodes have a low signal-to-noise ratio and a complex signal content. In this paper, we consi... | Unsupervised analysis of data recorded from the peripheral nervous system denoises and categorises signals. |
Adversarial attacks on convolutional neural networks (CNN) have gained significant attention and there have been active research efforts on defense mechanisms. Stochastic input transformation methods have been proposed, where the idea is to recover the image from adversarial attack by random transformation, and to take... | We enhance existing transformation-based defenses by using a distribution classifier on the distribution of softmax obtained from transformed images. |
Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism... | We propose an approach to learn decentralized policies in multi-agent settings using attention-based critics and demonstrate promising results in environments with complex interactions. |
The recent expansion of machine learning applications to molecular biology proved to have a significant contribution to our understanding of biological systems, and genome functioning in particular. Technological advances enabled the collection of large epigenetic datasets, including information about various DNA bind... | We apply RNN to solve the biological problem of chromatin folding patterns prediction from epigenetic marks and demonstrate for the first time that utilization of memory of sequential states on DNA molecule is significant for the best performance. |
Previous work showed empirically that large neural networks can be significantly reduced in size while preserving their accuracy. Model compression became a central research topic, as it is crucial for deployment of neural networks on devices with limited computational and memory resources. The majority of the compress... | We propose an efficient, provable and data independent method for network compression via neural pruning using coresets of neurons -- a novel construction proposed in this paper. |
Planning problems in partially observable environments cannot be solved directly with convolutional networks and require some form of memory. But, even memory networks with sophisticated addressing schemes are unable to learn intelligent reasoning satisfactorily due to the complexity of simultaneously learning to acces... | Memory Augmented Network to plan in partially observable environments. |
Contextualized word representations such as ELMo and BERT have become the de facto starting point for incorporating pretrained representations for downstream NLP tasks. In these settings, contextual representations have largely made obsolete their static embedding predecessors such as Word2Vec and GloVe. However, stati... | A procedure for distilling contextual models into static embeddings; we apply our method to 9 popular models and demonstrate clear gains in representation quality wrt Word2Vec/GloVe and improved analysis potential by thoroughly studying social bias. |
The brain performs unsupervised learning and (perhaps) simultaneous supervised learning. This raises the question as to whether a hybrid of supervised and unsupervised methods will produce better learning. Inspired by the rich space of Hebbian learning rules, we set out to directly learn the unsupervised learning rule ... | Metalearning unsupervised update rules for neural networks improves performance and potentially demonstrates how neurons in the brain learn without access to global labels. |
Deep convolutional networks often append additive constant ("bias") terms to their convolution operations, enabling a richer repertoire of functional mappings. Biases are also used to facilitate training, by subtracting mean response over batches of training images (a component of "batch normalization"). Recent state-o... | We show that removing constant terms from CNN architectures provides interpretability of the denoising method via linear-algebra techniques and also boosts generalization performance across noise levels. |
The selection of initial parameter values for gradient-based optimization of deep neural networks is one of the most impactful hyperparameter choices in deep learning systems, affecting both convergence times and model performance. Yet despite significant empirical and theoretical analysis, relatively little has been p... | We provide for the first time a rigorous proof that orthogonal initialization speeds up convergence relative to Gaussian initialization, for deep linear networks. |
Survival function estimation is used in many disciplines, but it is most common in medical analytics in the form of the Kaplan-Meier estimator. Sensitive data (patient records) is used in the estimation without any explicit control on the information leakage, which is a significant privacy concern. We propose a first d... | A first differentially private estimate of the survival function |
Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning. Although some label structure can implicitly be obtained when training on huge amounts of data, in a few-shot learning context where little data... | CAML is an instance of MAML with conditional class dependencies. |
We study the problem of multiset prediction. The goal of multiset prediction is to train a predictor that maps an input to a multiset consisting of multiple items. Unlike existing problems in supervised learning, such as classification, ranking and sequence generation, there is no known order among items in a target mu... | We study the problem of multiset prediction and propose a novel multiset loss function, providing analysis and empirical evidence that demonstrates its effectiveness. |
Understanding theoretical properties of deep and locally connected nonlinear network, such as deep convolutional neural network (DCNN), is still a hard problem despite its empirical success. In this paper, we propose a novel theoretical framework for such networks with ReLU nonlinearity. The framework bridges data dist... | This paper presents a theoretical framework that models data distribution explicitly for deep and locally connected ReLU network |
Multi-agent cooperation is an important feature of the natural world. Many tasks involve individual incentives that are misaligned with the common good, yet a wide range of organisms from bacteria to insects and humans are able to overcome their differences and collaborate. Therefore, the emergence of cooperative behav... | We introduce a biologically-inspired modular evolutionary algorithm in which deep RL agents learn to cooperate in a difficult multi-agent social game, which could help to explain the evolution of altruism. |
In adversarial attacks to machine-learning classifiers, small perturbations are added to input that is correctly classified. The perturbations yield adversarial examples, which are virtually indistinguishable from the unperturbed input, and yet are misclassified. In standard neural networks used for deep learning, atta... | We introduce a type of neural network that is structurally resistant to adversarial attacks, even when trained on unaugmented training sets. The resistance is due to the stability of network units wrt input perturbations. |
We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization.
Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accurac... | We show that adversarial robustness might come at the cost of standard classification performance, but also yields unexpected benefits. |
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issues. In essence, mixup trains a neural network on convex combinations of pairs of examples and their ... | Training on convex combinations between random training examples and their labels improves generalization in deep neural networks |
We present a novel approach to spike sorting for high-density multielectrode probes using the Neural Clustering Process (NCP), a recently introduced neural architecture that performs scalable amortized approximate Bayesian inference for efficient probabilistic clustering. To optimally encode spike waveforms for cluster... | We present a novel approach to spike sorting using the Neural Clustering Process (NCP), a recently introduced neural architecture that performs scalable amortized approximate Bayesian inference for efficient probabilistic clustering. |
The goal of the paper is to propose an algorithm for learning the most generalizable solution from given training data. It is shown that Bayesian approach leads to a solution that dependent on statistics of training data and not on particular
samples. The solution is stable under perturbations of training data because... | Proposed method for finding the most generalizable solution that is stable w.r.t. perturbations of trainig data. |
Humans rely on episodic memory constantly, in remembering the name of someone they met 10 minutes ago, the plot of a movie as it unfolds, or where they parked the car. Endowing reinforcement learning agents with episodic memory is a key step on the path toward replicating human-like general intelligence. We analyze why... | Implementing and evaluating episodic memory for RL. |
Parameters are one of the most critical components of machine learning models. As datasets and learning domains change, it is often necessary and time-consuming to re-learn entire models. Rather than re-learning the parameters from scratch, replacing learning with optimization, we propose a framework building upon the ... | We present a method of adapting hyperparameters of probabilistic models using optimal transport with applications in robotics |
An algorithm is introduced for learning a predictive state representation with off-policy temporal difference (TD) learning that is then used to learn to steer a vehicle with reinforcement learning. There are three components being learned simultaneously: (1) the off-policy predictions as a compact representation of... | An algorithm to learn a predictive state representation with general value functions and off-policy learning is applied to the problem of vision-based steering in autonomous driving. |
For numerous domains, including for instance earth observation, medical imaging, astrophysics,..., available image and signal datasets often irregular space-time sampling patterns and large missing data rates. These sampling properties is a critical issue to apply state-of-the-art learning-based (e.g., auto-encoders, C... | We address the end-to-end learning of energy-based representations for signal and image observation dataset with irregular sampling patterns. |
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood. Existing methods either neglect credit assignment to pre-adaptation behavior or implement it naively. This leads to poor sample-efficiency during meta-training as well as ineffective task identification strategies.
This paper provid... | A novel and theoretically grounded meta-reinforcement learning algorithm |
When training a neural network for a desired task, one may prefer to adapt a pretrained network rather than start with a randomly initialized one -- due to lacking enough training data, performing lifelong learning where the system has to learn a new task while being previously trained for other tasks, or wishing to en... | Side-tuning adapts a pre-trained network by training a lightweight "side" network that is fused with the (unchanged) pre-trained network using a simple additive process. |
In this paper, we present a technique for generating artificial datasets that retain statistical properties of the real data while providing differential privacy guarantees with respect to this data. We include a Gaussian noise layer in the discriminator of a generative adversarial network to make the output and the gr... | Train GANs with differential privacy to generate artificial privacy-preserving datasets. |
This paper presents two methods to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. Unlike convolutional studies that visualize image appearances corresponding to the network output or a neural activation from a global perspective, our research aims to clarify how a ce... | This paper presents methods to disentangle and interpret contextual effects that are encoded in a deep neural network. |
The main goal of network pruning is imposing sparsity on the neural network by increasing the number of parameters with zero value in order to reduce the architecture size and the computational speedup. | Proposing a novel method based on the guided attention to enforce the sparisty in deep neural networks. |
Giving provable guarantees for learning neural networks is a core challenge of machine learning theory. Most prior work gives parameter recovery guarantees for one hidden layer networks, however, the networks used in practice have multiple non-linear layers. In this work, we show how we can strengthen such results to d... | We provably recover the lowest layer in a deep neural network assuming that the lowest layer uses a "high threshold" activation and the above network is a "well-behaved" polynomial. |
Federated learning involves training and effectively combining machine learning models from distributed partitions of data (i.e., tasks) on edge devices, and be naturally viewed as a multi- task learning problem. While Federated Averaging (FedAvg) is the leading optimization method for training non-convex models in thi... | We introduce FedProx, a framework to tackle statistical heterogeneity in federated settings with convergence guarantees and improved robustness and stability. |
Referential games offer a grounded learning environment for neural agents which accounts for the fact that language is functionally used to communicate. However, they do not take into account a second constraint considered to be fundamental for the shape of human language: that it must be learnable by new language lear... | We enable both the cultural evolution of language and the genetic evolution of agents in a referential game, using a new Language Transmission Engine. |
The need for large amounts of training image data with clearly defined features is a major obstacle to applying generative adversarial networks(GAN) on image generation where training data is limited but diverse, since insufficient latent feature representation in the already scarce data often leads to instability and ... | We introduced a novel, simple, and efficient data augmentation method that boosts the performances of existing GANs when training data is limited and diverse. |
We develop a stochastic whole-brain and body simulator of the nematode roundworm Caenorhabditis elegans (C. elegans) and show that it is sufficiently regularizing to allow imputation of latent membrane potentials from partial calcium fluorescence imaging observations. This is the first attempt we know of to ``complete ... | We develop a whole-connectome and body simulator for C. elegans and demonstrate joint state-space and parameter inference in the simulator. |
The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node represent... | Inspired by CapsNet, we propose a novel architecture for graph embeddings on the basis of node features extracted from GNN. |
We introduce a novel framework for generative models based on Restricted Kernel Machines (RKMs) with multi-view generation and uncorrelated feature learning capabilities, called Gen-RKM. To incorporate multi-view generation, this mechanism uses a shared representation of data from various views. The mechanism is flexib... | Gen-RKM: a novel framework for generative models using Restricted Kernel Machines with multi-view generation and uncorrelated feature learning. |
Work on the problem of contextualized word representation—the development of reusable neural network components for sentence understanding—has recently seen a surge of progress centered on the unsupervised pretraining task of language modeling with methods like ELMo (Peters et al., 2018). This paper contributes the fi... | We compare many tasks and task combinations for pretraining sentence-level BiLSTMs for NLP tasks. Language modeling is the best single pretraining task, but simple baselines also do well. |
In this paper, we study the adversarial attack and defence problem in deep learning from the perspective of Fourier analysis. We first explicitly compute the Fourier transform of deep ReLU neural networks and show that there exist decaying but non-zero high frequency components in the Fourier spectrum of neural network... | An insight into the reason of adversarial vulnerability, an effective defense method against adversarial attacks. |
Reinforcement learning methods that continuously learn neural networks by episode generation with game tree search have been successful in two-person complete information deterministic games such as chess, shogi, and Go. However, there are only reports of practical cases and there are little evidence to guarantee the s... | Apply Monte Carlo Tree Search to episode generation in Alpha Zero |
Message-passing neural networks (MPNNs) have been successfully applied in a wide variety of applications in the real world. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the abilit... | For graph neural networks, the aggregation on a graph can benefit from a continuous space underlying the graph. |
We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We introduce a novel iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated programs s... | We use a simple search algorithm involving an RNN and priority queue to find solutions to coding tasks. |
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained ... | We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcoming of previous spectral graph CNN methods that depend on graph Fourier transform. |
Learning rate decay (lrDecay) is a \emph{de facto} technique for training modern neural networks. It starts with a large learning rate and then decays it multiple times. It is empirically observed to help both optimization and generalization. Common beliefs in how lrDecay works come from the optimization analysis of (S... | We provide another novel explanation of learning rate decay: an initially large learning rate suppresses the network from memorizing noisy data while decaying the learning rate improves the learning of complex patterns. |
We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning. We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting. We propose an exploration strategy based on upper-confidence bounds (UCB). Our experimen... | Adapting UCB exploration to ensemble Q-learning improves over prior methods such as Double DQN, A3C+ on Atari benchmark |
We focus on the problem of learning a single motor module that can flexibly express a range of behaviors for the control of high-dimensional physically simulated humanoids. To do this, we propose a motor architecture that has the general structure of an inverse model with a latent-variable bottleneck. We show that it i... | Neural Probabilistic Motor Primitives compress motion capture tracking policies into one flexible model capable of one-shot imitation and reuse as a low-level controller. |
Data augmentation is a useful technique to enlarge the size of the training set and prevent overfitting for different machine learning tasks when training data is scarce. However, current data augmentation techniques rely heavily on human design and domain knowledge, and existing automated approaches are yet to fully e... | We present an automated adaptive data augmentation that works for multiple different tasks. |
Deep neural networks with millions of parameters may suffer from poor generalizations due to overfitting. To mitigate the issue, we propose a new regularization method that penalizes the predictive distribution between similar samples. In particular, we distill the predictive distribution between different samples of t... | We propose a new regularization technique based on the knowledge distillation. |
In this paper, we consider the specific problem of word-level language modeling and investigate strategies for regularizing and optimizing LSTM-based models. We propose the weight-dropped LSTM, which uses DropConnect on hidden-to-hidden weights, as a form of recurrent regularization. Further, we introduce NT-ASGD, a no... | Effective regularization and optimization strategies for LSTM-based language models achieves SOTA on PTB and WT2. |
Various methods of measuring unit selectivity have been developed with the aim of better understanding how neural networks work. But the different measures provide divergent estimates of selectivity, and this has led to different conclusions regarding the conditions in which selective object representations are learn... | Looking for object detectors using many different selectivity measures; CNNs are slightly selective , but not enough to be termed object detectors. |
The folding structure of the DNA molecule combined with helper molecules, also referred to as the chromatin, is highly relevant for the functional properties of DNA. The chromatin structure is largely determined by the underlying primary DNA sequence, though the interaction is not yet fully understood. In this paper we... | A method to transform DNA sequences into 2D images using space-filling Hilbert Curves to enhance the strengths of CNNs |
This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster. The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform... | A learnable clustering objective to facilitate transfer learning across domains and tasks |
Recent advances in cross-lingual word embeddings have primarily relied on mapping-based methods, which project pretrained word embeddings from different languages into a shared space through a linear transformation. However, these approaches assume word embedding spaces are isomorphic between different languages, which... | Joint method for learning cross-lingual embeddings with state-of-art performance for cross-lingual tasks and mono-lingual quality |
To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state representing the condition of the world; (b) it should form a belief which represents uncertainty on the world; (c) it should go beyond simple step-... | Generative model of temporal data, that builds online belief state, operates in latent space, does jumpy predictions and rollouts of states. |
This paper introduces an information theoretic co-training objective for unsupervised learning. We consider the problem of predicting the future . Rather than predict future sensations (image pixels or sound waves) we predict ``hypotheses'' to be confirmed by future sensations . More formally, we assume a population d... | Presents an information theoretic training objective for co-training and demonstrates its power in unsupervised learning of phonetics. |
Generative models for singing voice have been mostly concerned with the task of "singing voice synthesis," i.e., to produce singing voice waveforms given musical scores and text lyrics. In this work, we explore a novel yet challenging alternative: singing voice generation without pre-assigned scores and lyrics, in both... | Our models generate singing voices without lyrics and scores. They take accompaniment as input and output singing voices. |
The carbon footprint of natural language processing (NLP) research has been increasing in recent years due to its reliance on large and inefficient neural network implementations. Distillation is a network compression technique which attempts to impart knowledge from a large model to a smaller one. We use teacher-stude... | We increase the efficiency of neural network dependency parsers with teacher-student distillation. |
While autoencoders are a key technique in representation learning for continuous structures, such as images or wave forms, developing general-purpose autoencoders for discrete structures, such as text sequence or discretized images, has proven to be more challenging. In particular, discrete inputs make it more difficul... | Adversarially Regularized Autoencoders learn smooth representations of discrete structures allowing for interesting results in text generation, such as unaligned style transfer, semi-supervised learning, and latent space interpolation and arithmetic. |
When data arise from multiple latent subpopulations, machine learning frameworks typically estimate parameter values independently for each sub-population. In this paper, we propose
to overcome these limits by considering samples as tasks in a multitask learning framework. | We present a method to estimate collections of regression models in which each model is personalized to a single sample. |
In this work, we first conduct mathematical analysis on the memory, which is
defined as a function that maps an element in a sequence to the current output,
of three RNN cells; namely, the simple recurrent neural network (SRN), the long
short-term memory (LSTM) and the gated recurrent unit (GRU). Based on the
analy... | A recurrent neural network cell with extended-long short-term memory and a multi-task RNN model for sequence-in-sequence-out problems |
Many recently trained neural networks employ large numbers of parameters to achieve good performance. One may intuitively use the number of parameters required as a rough gauge of the difficulty of a problem. But how accurate are such notions? How many parameters are really needed? In this paper we attempt to answer th... | We train in random subspaces of parameter space to measure how many dimensions are really needed to find a solution. |
Graph Neural Networks as a combination of Graph Signal Processing and Deep Convolutional Networks shows great power in pattern recognition in non-Euclidean domains. In this paper, we propose a new method to deploy two pipelines based on the duality of a graph to improve accuracy. By exploring the primal graph and its d... | A primal dual graph neural network model for semi-supervised learning |
We describe two end-to-end autoencoding models for semi-supervised graph-based dependency parsing. The first model is a Local Autoencoding Parser (LAP) encoding the input using continuous latent variables in a sequential manner; The second model is a Global Autoencoding Parser (GAP) encoding the input into dependency t... | We describe two end-to-end autoencoding parsers for semi-supervised graph-based dependency parsing. |
We improve previous end-to-end differentiable neural networks (NNs) with fast
weight memories. A gate mechanism updates fast weights at every time step of
a sequence through two separate outer-product-based matrices generated by slow
parts of the net. The system is trained on a complex sequence to sequence variation... | An improved Fast Weight network which shows better results on a general toy task. |
The field of deep learning has been craving for an optimization method that shows outstanding property for both optimization and generalization. We propose a method for mathematical optimization based on flows along geodesics, that is, the shortest paths between two points, with respect to the Riemannian metric induc... | Introduction of a new optimization method and its application to deep learning. |
We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and are particularly suitable to be executed on distributed quantum systems over a quantum network. Along with this general class of ansatze, w... | Introducing a new class of quantum neural networks for learning graph-based representations on quantum computers. |
Data breaches involve information being accessed by unauthorized parties. Our research concerns user perception of data breaches, especially issues relating to accountability. A preliminary study indicated many people had weak understanding of the issues, and felt they themselves were somehow responsible. We speculated... | "In this paper, we tested communication strategies' influence on users mental models of a data breach." |
Goal recognition is the problem of inferring the correct goal towards which an agent executes a plan, given a set of goal hypotheses, a domain model, and a (possibly noisy) sample of the plan being executed.
This is a key problem in both cooperative and competitive agent interactions and recent approaches have prod... | A goal recognition approach based on operator counting heuristics used to account for noise in the dataset. |
It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts. To help address this, we propose using knowledge distillation where single-task models teach a multi-task model. We enhance this training with teacher annealing, a novel method that gradually transiti... | distilling single-task models into a multi-task model improves natural language understanding performance |
The detection of out of distribution samples for image classification has been widely researched. Safety critical applications, such as autonomous driving, would benefit from the ability to localise the unusual objects causing the image to be out of distribution. This paper adapts state-of-the-art methods for detecting... | Evaluating pixel-level out-of-distribution detection methods on two new real world datasets using PSPNet and DeeplabV3+. |
We present a domain adaptation method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent adversarial methods proposed for this task learn to align features across domains by ``fooling'' a special domain classifier network. However, a drawback of this approach is t... | We present a new adversarial method for adapting neural representations based on a critic that detects non-discriminative features. |
The use of deep learning for a wide range of data problems has increased the need for understanding and diagnosing these models, and deep learning interpretation techniques have become an essential tool for data analysts. Although numerous model interpretation methods have been proposed in recent years, most of these p... | We propose a statistical framework and a theoretically consistent procedure for saliency estimation. |
We explore the idea of compositional set embeddings that can be used to infer not
just a single class, but the set of classes associated with the input data (e.g., image,
video, audio signal). This can be useful, for example, in multi-object detection in
images, or multi-speaker diarization (one-shot learning) in au... | We explored how a novel method of compositional set embeddings can both perceive and represent not just a single class but an entire set of classes that is associated with the input data. |
In this work, we propose a goal-driven collaborative task that contains language, vision, and action in a virtual environment as its core components. Specifically, we develop a Collaborative image-Drawing game between two agents, called CoDraw. Our game is grounded in a virtual world that contains movable clip art obje... | We introduce a dataset, models, and training + evaluation protocols for a collaborative drawing task that allows studying goal-driven and perceptually + actionably grounded language generation and understanding. |
Presence of bias and confounding effects is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in the recent years. Such challenges range from spurious associations of confounding variables in medical studies to the bias of race in gender or face recognit... | We propose a method based on the adversarial training strategy to learn discriminative features unbiased and invariant to the confounder(s) by incorporating a loss function that encourages a vanished correlation between the bias and learned features. |
Existing neural question answering (QA) models are required to reason over and draw complicated inferences from a long context for most large-scale QA datasets. However, if we view QA as a combined retrieval and reasoning task, we can assume the existence of a minimal context which is necessary and sufficient to answer... | A modular approach consisting of a sentence selector module followed by the QA model can be made more robust to adversarial attacks in comparison to a QA model trained on full context. |
Multi-relational graph embedding which aims at achieving effective representations with reduced low-dimensional parameters, has been widely used in knowledge base completion. Although knowledge base data usually contains tree-like or cyclic structure, none of existing approaches can embed these data into a compatible s... | Multi-relational graph embedding with Riemannian manifolds and TransE-like loss function. |
Catastrophic forgetting in neural networks is one of the most well-known problems in continual learning. Previous attempts on addressing the problem focus on preventing important weights from changing. Such methods often require task boundaries to learn effectively and do not support backward transfer learning. In this... | We propose a meta learning algorithm for continual learning which can effectively prevent catastrophic forgetting problem and support backward transfer learning. |
We give a formal procedure for computing preimages of convolutional
network outputs using the dual basis defined from the set of
hyperplanes associated with the layers of the network. We point out
the special symmetry associated with arrangements of hyperplanes of
convolutional networks that take the form of r... | Analysis of deep convolutional networks in terms of associated arrangement of hyperplanes |
Meta learning has been making impressive progress for fast model adaptation. However, limited work has been done on learning fast uncertainty adaption for Bayesian modeling. In this paper, we propose to achieve the goal by placing meta learning on the space of probability measures, inducing the concept of meta sampling... | We proposed a Bayesian meta sampling method for adapting the model uncertainty in meta learning |
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