query stringlengths 273 149k | pos list | neg list | task stringclasses 1
value | instruction dict |
|---|---|---|---|---|
In order to mimic the human ability of continual acquisition and transfer of knowledge across various tasks, a learning system needs the capability for life-long learning, effectively utilizing the previously acquired skills. As such, the key challenge is to transfer and generalize the knowledge learned from one task t... | [
"A new method uses statistical leverage score information to measure the importance of the data samples in every task and adopts frequent directions approach to enable a life-long learning property."
] | [
"we define the filter-level pruning problem for binary neural networks for the first time and propose method to solve it."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Convolutional neural networks (CNNs) are inherently equivariant to translation. Efforts to embed other forms of equivariance have concentrated solely on rotation. We expand the notion of equivariance in CNNs through the Polar Transformer Network (PTN). PTN combines ideas from the Spatial Transformer Network (STN) and c... | [
"We learn feature maps invariant to translation, and equivariant to rotation and scale."
] | [
"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."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task. Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks. Here, we reformulat... | [
"A specific gradient-based meta-learning algorithm, MAML, is equivalent to an inference procedure in a hierarchical Bayesian model. We use this connection to improve MAML via methods from approximate inference and curvature estimation."
] | [
"We show that posterior collapse in linear VAEs is caused entirely by marginal log-likelihood (not ELBO). Experiments on deep VAEs suggest a similar phenomenon is at play."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
This work provides an automatic machine learning (AutoML) modelling architecture called Autostacker. Autostacker improves the prediction accuracy of machine learning baselines by utilizing an innovative hierarchical stacking architecture and an efficient parameter search algorithm. Neither prior domain knowledge about ... | [
"Automate machine learning system with efficient search algorithm and innovative structure to provide better model baselines."
] | [
"Our proposed algorithm does not use all of the unlabeled data for the training, and it rather uses them selectively."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Surrogate models can be used to accelerate approximate Bayesian computation (ABC). In one such framework the discrepancy between simulated and observed data is modelled with a Gaussian process. So far principled strategies have been proposed only for sequential selection of the simulation locations. To address this lim... | [
"We propose principled batch Bayesian experimental design strategies and a method for uncertainty quantification of the posterior summaries in a Gaussian process surrogate-based approximate Bayesian computation framework."
] | [
"General analysis of sign-based methods (e.g. signSGD) for non-convex optimization, built on intuitive bounds on success probabilities."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Randomly initialized first-order optimization algorithms are the method of choice for solving many high-dimensional nonconvex problems in machine learning, yet general theoretical guarantees cannot rule out convergence to critical points of poor objective value. For some highly structured nonconvex problems however, th... | [
"We provide an efficient convergence rate for gradient descent on the complete orthogonal dictionary learning objective based on a geometric analysis."
] | [
"We show that posterior collapse in linear VAEs is caused entirely by marginal log-likelihood (not ELBO). Experiments on deep VAEs suggest a similar phenomenon is at play."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age. Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century. In this paper we study whether it is possible to automate the disco... | [
"We show that creatively designed and trained RNN architectures can decode well known sequential codes and achieve close to optimal performances."
] | [
"We present eligibility propagation an alternative to BPTT that is compatible with experimental data on synaptic plasticity and competes with BPTT on machine learning benchmarks."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, but their efficiency turns out to be unsatisfactory in practice. In this paper, we provide a new insight into the non-convergence issue of Ada... | [
"We analysis and solve the non-convergence issue of Adam."
] | [
"A backward model of previous (state, action) given the next state, i.e. P(s_t, a_t | s_{t+1}), can be used to simulate additional trajectories terminating at states of interest! Improves RL learning efficiency."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Most domain adaptation methods consider the problem of transferring knowledge to the target domain from a single source dataset. However, in practical applications, we typically have access to multiple sources. In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adv... | [
"In this paper we propose generative method for multisource domain adaptation based on decomposition of content, style and domain factors."
] | [
"We leverage deterministic autoencoders as generative models by proposing mixing functions which combine hidden states from pairs of images. These mixes are made to look realistic through an adversarial framework."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Inferring the most likely configuration for a subset of variables of a joint distribution given the remaining ones – which we refer to as co-generation – is an important challenge that is computationally demanding for all but the simplest settings. This task has received a considerable amount of attention, particularly... | [
"Using annealed importance sampling on the co-generation problem. "
] | [
"A backward model of previous (state, action) given the next state, i.e. P(s_t, a_t | s_{t+1}), can be used to simulate additional trajectories terminating at states of interest! Improves RL learning efficiency."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Implicit probabilistic models are models defined naturally in terms of a sampling procedure and often induces a likelihood function that cannot be expressed explicitly. We develop a simple method for estimating parameters in implicit models that does not require knowledge of the form of the likelihood function or any d... | [
"We develop a new likelihood-free parameter estimation method that is equivalent to maximum likelihood under some conditions"
] | [
"We show that posterior collapse in linear VAEs is caused entirely by marginal log-likelihood (not ELBO). Experiments on deep VAEs suggest a similar phenomenon is at play."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on estimating a reward function that best explains an expert agent’s policy or demonstrated behavior on a control task, it is often the case that such behavior is more succinctly represented by a simple reward combined with a set of hard... | [
"Our method infers constraints on task execution by leveraging the principle of maximum entropy to quantify how demonstrations differ from expected, un-constrained behavior."
] | [
"We introduce the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended to policy gradient methods."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning. In this work, we discuss the required elements of a robotic system that can continually and autonomously improve with data collected in ... | [
"System to learn robotic tasks in the real world with reinforcement learning without instrumentation"
] | [
"We address the task of autonomous exploration and navigation using spatial affordance maps that can be learned in a self-supervised manner, these outperform classic geometric baselines while being more sample efficient than contemporary RL algorithms"
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
We study the problem of cross-lingual voice conversion in non-parallel speech corpora and one-shot learning setting. Most prior work require either parallel speech corpora or enough amount of training data from a target speaker. However, we convert an arbitrary sentences of an arbitrary source speaker to target speaker... | [
"We use a Variational Autoencoder to separate style and content, and achieve voice conversion by modifying style embedding and decoding. We investigate using a multi-language speech corpus and investigate its effects."
] | [
"A residual EBM for text whose formulation is equivalent to discriminating between human and machine generated text. We study its generalization behavior."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification. The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of... | [
"The paper proposes a method for forcing CNNs to leverage spatial attention in learning more object-centric representations that perform better in various respects."
] | [
"We present a new approach, SNIP, that is simple, versatile and interpretable; it prunes irrelevant connections for a given task at single-shot prior to training and is applicable to a variety of neural network models without modifications."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Recurrent neural network(RNN) is an effective neural network in solving very complex supervised and unsupervised tasks. There has been a significant improvement in RNN field such as natural language processing, speech processing, computer vision and other multiple domains. This paper deals with RNN application on diffe... | [
"Recurrent neural networks for Cybersecurity use-cases"
] | [
"Data stream algorithms can be improved using deep learning, while retaining performance guarantees."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Anatomical studies demonstrate that brain reformats input information to generate reliable responses for performing computations. However, it remains unclear how neural circuits encode complex spatio-temporal patterns. We show that neural dynamics are strongly influenced by the phase alignment between the input and the... | [
"Input Structuring along Chaos for Stability"
] | [
"We detect statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Generative adversarial networks are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions. GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t. the generative parameters, and thus do not w... | [
"We address training GANs with discrete data by formulating a policy gradient that generalizes across f-divergences"
] | [
"An empirical study of variational inference based on chi-square divergence minimization, showing that minimizing the CUBO is trickier than maximizing the ELBO"
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exasperated in problems with long horizons or high-dimensional action spaces. To mitigate this issue, we derive a bias-free action-dependent ba... | [
"Action-dependent baselines can be bias-free and yield greater variance reduction than state-only dependent baselines for policy gradient methods."
] | [
"We propose a model-based method called \"Search with Amortized Value Estimates\" (SAVE) which leverages both real and planned experience by combining Q-learning with Monte-Carlo Tree Search, achieving strong performance with very small search budgets."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches. The problem is further exacerbated when supervised learning is applied to a number of correlated tasks simultaneously since the amount of labels required scales with the number of tasks. To mitigate this concern... | [
"We propose an active multitask learning algorithm that achieves knowledge transfer between tasks."
] | [
"Our proposed algorithm does not use all of the unlabeled data for the training, and it rather uses them selectively."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Detection of photo manipulation relies on subtle statistical traces, notoriously removed by aggressive lossy compression employed online. We demonstrate that end-to-end modeling of complex photo dissemination channels allows for codec optimization with explicit provenance objectives. We design a lightweight trainable l... | [
"We learn an efficient lossy image codec that can be optimized to facilitate reliable photo manipulation detection at fractional cost in payload/quality and even at low bitrates."
] | [
"We propose accelerating Batch Normalization (BN) through sampling less correlated data for reduction operations with regular execution pattern, which achieves up to 2x and 20% speedup for BN itself and the overall training, respectively."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Recurrent Neural Networks have long been the dominating choice for sequence modeling. However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure. Therefore, many non-recurrent sequence models that are built on convolutio... | [
"This paper proposes an effective generic sequence model which leverages the strengths of both RNNs and Multi-head attention."
] | [
"We present a new approach, SNIP, that is simple, versatile and interpretable; it prunes irrelevant connections for a given task at single-shot prior to training and is applicable to a variety of neural network models without modifications."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Many tasks in natural language processing and related domains require high precision output that obeys dataset-specific constraints. This level of fine-grained control can be difficult to obtain in large-scale neural network models. In this work, we propose a structured latent-variable approach that adds discrete contr... | [
"A structured latent-variable approach that adds discrete control states within a standard autoregressive neural paradigm to provide arbitrary grounding of internal model decisions, without sacrificing any representational power of neural models."
] | [
"A fully connected architecture is used to produce word embeddings from character representations, outperforms traditional embeddings and provides insight into sparsity and dropout."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Suppose a deep classification model is trained with samples that need to be kept private for privacy or confidentiality reasons. In this setting, can an adversary obtain the private samples if the classification model is given to the adversary? We call this reverse engineering against the classification model the Class... | [
"Estimation of training data distribution from trained classifier using GAN."
] | [
"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. "
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
The goal of standard compressive sensing is to estimate an unknown vector from linear measurements under the assumption of sparsity in some basis. Recently, it has been shown that significantly fewer measurements may be required if the sparsity assumption is replaced by the assumption that the unknown vector lies near ... | [
"We establish that the scaling laws derived in (Bora et al., 2017) are optimal or near-optimal in the absence of further assumptions."
] | [
"We prove that for a large class of functions f there exists an interval certified robust network approximating f up to arbitrary precision."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents. Here we explore whether modern deep reinforcement learning can be used to train agents to perform causal reasoning. We adopt a meta-learning approach, where the agent learns a policy for conducting experime... | [
"meta-learn a learning algorithm capable of causal reasoning"
] | [
"A backward model of previous (state, action) given the next state, i.e. P(s_t, a_t | s_{t+1}), can be used to simulate additional trajectories terminating at states of interest! Improves RL learning efficiency."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews. To employ rule based sentiment classification, we require sentiment lexicons. However, manual construction of sentiment lexicon i... | [
"Corpus based Algorithm is developed generate Amharic Sentiment lexicon relying on corpus"
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL). In the tabular case, all provably efficient model-free algorithms rely on it. However, model-free deep RL algorithms do not use optimistic initialisation despite taking inspiration from these provably efficient ... | [
"We augment the Q-value estimates with a count-based bonus that ensures optimism during action selection and bootstrapping, even if the Q-value estimates are pessimistic."
] | [
"A backward model of previous (state, action) given the next state, i.e. P(s_t, a_t | s_{t+1}), can be used to simulate additional trajectories terminating at states of interest! Improves RL learning efficiency."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning... | [
"We propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework."
] | [
"Parameter space noise allows reinforcement learning algorithms to explore by perturbing parameters instead of actions, often leading to significantly improved exploration performance."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
In many settings, it is desirable to learn decision-making and control policies through learning or from expert demonstrations. The most common approaches under this framework are Behaviour Cloning (BC), and Inverse Reinforcement Learning (IRL). Recent methods for IRL have demonstrated the capacity to learn effective p... | [
"Distribution matching through divergence minimization provides a common ground for comparing adversarial Maximum-Entropy Inverse Reinforcement Learning methods to Behaviour Cloning."
] | [
"An empirical study of variational inference based on chi-square divergence minimization, showing that minimizing the CUBO is trickier than maximizing the ELBO"
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Value-based methods constitute a fundamental methodology in planning and deep reinforcement learning (RL). In this paper, we propose to exploit the underlying structures of the state-action value function, i.e., Q function, for both planning and deep RL. In particular, if the underlying system dynamics lead to some glo... | [
"We propose a generic framework that allows for exploiting the low-rank structure in both planning and deep reinforcement learning."
] | [
"To solve the gradient vanishing/exploding problems, we proprose an efficient parametrization of the transition matrix of RNN that loses no expressive power, converges faster and has good generalization."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Learned representations of source code enable various software developer tools, e.g., to detect bugs or to predict program properties. At the core of code representations often are word embeddings of identifier names in source code, because identifiers account for the majority of source code vocabulary and convey impor... | [
"A benchmark to evaluate neural embeddings of identifiers in source code."
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget. This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions. F... | [
"Rearranging the terms in maximum mean discrepancy yields a much better loss function for the discriminator of generative adversarial nets"
] | [
"We show that posterior collapse in linear VAEs is caused entirely by marginal log-likelihood (not ELBO). Experiments on deep VAEs suggest a similar phenomenon is at play."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Deep neural networks have shown incredible performance for inference tasks in a variety of domains. Unfortunately, most current deep networks are enormous cloud-based structures that require significant storage space, which limits scaling of deep learning as a service (DLaaS) and use for on-device augmented intelligenc... | [
"This paper finds algorithms that directly use lossless compressed representations of deep feedforward networks, to perform inference without full decompression."
] | [
"We show shortcut connections should be placed in patterns that minimize between-layer distances during backpropagation, and design networks that achieve log L distances using L log(L) connections."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train. One common way to tackle this issue has been to propose new formulations of the GAN objective. Yet, surprisingly few studies have looked at optimization methods desi... | [
"We cast GANs in the variational inequality framework and import techniques from this literature to optimize GANs better; we give algorithmic extensions and empirically test their performance for training GANs."
] | [
"We investigate the convergence of popular optimization algorithms like Adam , RMSProp and propose new variants of these methods which provably converge to optimal solution in convex settings. "
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods. In addition, cu... | [
"Addressing task heterogeneity problem in meta-learning by introducing meta-knowledge graph"
] | [
"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."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
In this paper, a deep boosting algorithm is developed to learn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs (base experts) with diverse capabilities, e.g., these base deep CNNs are sequentially trained to recognize a set of object classes in an easy-to-hard way according to th... | [
" A deep boosting algorithm is developed to learn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs."
] | [
"We present a new approach, SNIP, that is simple, versatile and interpretable; it prunes irrelevant connections for a given task at single-shot prior to training and is applicable to a variety of neural network models without modifications."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
We present a method for translating music across musical instruments and styles. This method is based on unsupervised training of a multi-domain wavenet autoencoder, with a shared encoder and a domain-independent latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net ca... | [
"An automatic method for converting music between instruments and styles"
] | [
"We leverage deterministic autoencoders as generative models by proposing mixing functions which combine hidden states from pairs of images. These mixes are made to look realistic through an adversarial framework."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Most existing defenses against adversarial attacks only consider robustness to L_p-bounded distortions. In reality, the specific attack is rarely known in advance and adversaries are free to modify images in ways which lie outside any fixed distortion model; for example, adversarial rotations lie outside the set of L_p... | [
"We propose several new attacks and a methodology to measure robustness against unforeseen adversarial attacks."
] | [
"Redistributing and growing weights according to the momentum magnitude enables the training of sparse networks from random initializations that can reach dense performance levels with 5% to 50% weights while accelerating training by up to 5.6x."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Deep neural networks (DNNs) have witnessed as a powerful approach in this year by solving long-standing Artificial intelligence (AI) supervised and unsupervised tasks exists in natural language processing, speech processing, computer vision and others. In this paper, we attempt to apply DNNs on three different cyber se... | [
"Deep-Net: Deep Neural Network for Cyber Security Use Cases"
] | [
"Information about whether a neural network's output will be correct or incorrect is somewhat present in the outputs of the network's intermediate layers."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations. Current approaches to decomposing demonstrations into primitives often assume manually defined primitives and bypass the difficulty of discovering these primitives. On the other hand,... | [
"We learn a space of motor primitives from unannotated robot demonstrations, and show these primitives are semantically meaningful and can be composed for new robot tasks."
] | [
"We propose a novel Intrinsically Motivated Goal Exploration architecture with unsupervised learning of goal space representations, and evaluate how various implementations enable the discovery of a diversity of policies."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Using modern deep learning models to make predictions on time series data from wearable sensors generally requires large amounts of labeled data. However, labeling these large datasets can be both cumbersome and costly. In this paper, we apply weak supervision to time series data, and programmatically label a dataset f... | [
"We demonstrate the feasibility of a weakly supervised time series classification approach for wearable sensor data. "
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Learning semantic correspondence between the structured data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation. Recent neural generation methods require to use large scale training data. However, the collected data-text pairs for training... | [
"We propose a local-to-global alignment framework to learn semantic correspondences from noisy data-text pairs with weak supervision"
] | [
"We provide insightful understanding of sequence-labeling NER and propose to use two types of cross structures, both of which bring theoretical and empirical improvements."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Imitation learning algorithms provide a simple and straightforward approach for training control policies via standard supervised learning methods. By maximizing the likelihood of good actions provided by an expert demonstrator, supervised imitation learning can produce effective policies without the algorithmic comple... | [
"Learning how to reach goals from scratch by using imitation learning with data relabeling"
] | [
"DCEM learns latent domains for optimization problems and helps bridge the gap between model-based and model-free RL --- we create a differentiable controller and fine-tune parts of it with PPO"
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Neural networks have recently shown excellent performance on numerous classi- fication tasks. These networks often have a large number of parameters and thus require much data to train. When the number of training data points is small, however, a network with high flexibility will quickly overfit the training data, ing... | [
"In the paper, we proposed an ensemble method called InterBoost for training neural networks for small-sample classification. The method has better generalization performance than other ensemble methods, and reduces variances significantly."
] | [
"It's important to consider optimization in function space, not just parameter space. We introduce a learning rule that reduces distance traveled in function space, just like SGD limits distance traveled in parameter space."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Interpreting neural networks is a crucial and challenging task in machine learning. In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights. Depending on the desired interactions, our method can a... | [
"We detect statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights."
] | [
"We find that deep networks which generalize poorly are more reliant on single directions than those that generalize well, and evaluate the impact of dropout and batch normalization, as well as class selectivity on single direction reliance."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforcement learning. Despite its apparent successes in these settings, to the best of our knowledge there has been no systematic exploration of its ... | [
"We benchmark the neural linear model on the UCI and UCI \"gap\" datasets."
] | [
"We find that deep networks which generalize poorly are more reliant on single directions than those that generalize well, and evaluate the impact of dropout and batch normalization, as well as class selectivity on single direction reliance."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
The reproducibility of reinforcement-learning research has been highlighted as a key challenge area in the field. In this paper, we present a case study in reproducing the of one groundbreaking algorithm, AlphaZero, a reinforcement learning system that learns how to play Go at a superhuman level given only the rules of... | [
"We reproduced AlphaZero on Google Cloud Platform"
] | [
"An optimization algorithm that explores various batch sizes based on probability and automatically exploits successful batch size which minimizes validation loss."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Generative adversarial networks (GANs) train implicit generative models through solving minimax problems. Such minimax problems are known as nonconvex- nonconcave, for which the dynamics of first-order methods are not well understood. In this paper, we consider GANs in the type of the integral probability metrics (IPMs... | [
"We establish global convergence to optimality for IPM-based GANs where the generator is an overparametrized neural network. "
] | [
"See the abstract. (For the revision, the paper is identical, except for a 59 page Supplementary Material, which can serve as a stand-along technical report version of the paper.)"
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram. Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-s... | [
"We develop efficient multi-scale approximate attributed network embedding procedures with provable properties."
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difficult. In this pap... | [
" A detailed empirical study in few-shot classification that revealing challenges in standard evaluation setting and showing a new direction."
] | [
"Pseudo-labeling has shown to be a weak alternative for semi-supervised learning. We, conversely, demonstrate that dealing with confirmation bias with several regularizations makes pseudo-labeling a suitable approach."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning. Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset -- i.e., finding specifications that are satisfied by all pla... | [
"We present a Bayesian inference model to infer contrastive explanations (as LTL specifications) describing how two sets of plan traces differ."
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
This work tackles the problem of characterizing and understanding the decision boundaries of neural networks with piece-wise linear non-linearity activations. We use tropical geometry, a new development in the area of algebraic geometry, to provide a characterization of the decision boundaries of a simple neural networ... | [
"Tropical geometry can be leveraged to represent the decision boundaries of neural networks and bring to light interesting insights."
] | [
"Deep architectures for 3D point clouds that are equivariant to SO(3) rotations, as well as translations and permutations. "
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks. Second-order methods, despite their better convergence rate, are rarely used in practice due to the pro- hibitive computational cost in calculating the second-order information. In this ... | [
"A novel Gram-Gauss-Newton method to train neural networks, inspired by neural tangent kernel and Gauss-Newton method, with fast convergence speed both theoretically and experimentally."
] | [
"An empirical study of variational inference based on chi-square divergence minimization, showing that minimizing the CUBO is trickier than maximizing the ELBO"
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Recent pretrained sentence encoders achieve state of the art on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures? We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA;), which we use to investigate the grammatical kn... | [
"We investigate the implicit syntactic knowledge of sentence embeddings using a new analysis set of grammatically annotated sentences with acceptability judgments."
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
When considering simultaneously a finite number of tasks, multi-output learning enables one to account for the similarities of the tasks via appropriate regularizers. We propose a generalization of the classical setting to a continuum of tasks by using vector-valued RKHSs. Several fundamental problems in machine learni... | [
"We propose an extension of multi-output learning to a continuum of tasks using operator-valued kernels."
] | [
"This paper 1) characterizes functions representable by ReLU DNNs, 2) formally studies the benefit of depth in such architectures, 3) gives an algorithm to implement empirical risk minimization to global optimality for two layer ReLU nets."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
We analyze the joint probability distribution on the lengths of the vectors of hidden variables in different layers of a fully connected deep network, when the weights and biases are chosen randomly according to Gaussian distributions, and the input is binary-valued. We show that, if the activation function satisfies a... | [
"We prove that, for activation functions satisfying some conditions, as a deep network gets wide, the lengths of the vectors of hidden variables converge to a length map."
] | [
"A backward model of previous (state, action) given the next state, i.e. P(s_t, a_t | s_{t+1}), can be used to simulate additional trajectories terminating at states of interest! Improves RL learning efficiency."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Data augmentation is one of the most effective approaches for improving the accuracy of modern machine learning models, and it is also indispensable to train a deep model for meta-learning. However, most current data augmentation implementations applied in meta-learning are the same as those used in the conventional im... | [
"We propose a data augmentation approach for meta-learning and prove that it is valid."
] | [
"Our proposed algorithm does not use all of the unlabeled data for the training, and it rather uses them selectively."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network, significantly extending prior work on a method known as ``Bayesian Dark Knowledge. " Our generalized framework applies to the case of classification models and takes as... | [
"A general framework for distilling Bayesian posterior expectations for deep neural networks."
] | [
"We show that posterior collapse in linear VAEs is caused entirely by marginal log-likelihood (not ELBO). Experiments on deep VAEs suggest a similar phenomenon is at play."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Variational Autoencoders (VAEs) have proven to be powerful latent variable models. How- ever, the form of the approximate posterior can limit the expressiveness of the model. Categorical distributions are flexible and useful building blocks for example in neural memory layers. We introduce the Hierarchical Discrete Var... | [
"In this paper, we introduce a discrete hierarchy of categorical latent variables that we train using the Concrete/Gumbel-Softmax relaxation and we derive an upper bound for the absolute difference between the unbiased and the biased objective."
] | [
"This paper 1) characterizes functions representable by ReLU DNNs, 2) formally studies the benefit of depth in such architectures, 3) gives an algorithm to implement empirical risk minimization to global optimality for two layer ReLU nets."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
In this paper, we propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term \emph{PowerSGD}. The proposed PowerSGD method simply raises the stochastic gradient to a certain power $\gamma\in$ during iterations and introduces only one additional parameter, ... | [
"We propose a new class of optimizers for accelerated non-convex optimization via a nonlinear gradient transformation. "
] | [
"We investigate the convergence of popular optimization algorithms like Adam , RMSProp and propose new variants of these methods which provably converge to optimal solution in convex settings. "
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
We aim to build complex humanoid agents that integrate perception, motor control, and memory. In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision. We develop an architecture capable of surprisingly flexible, ... | [
"Solve tasks involving vision-guided humanoid locomotion, reusing locomotion behavior from motion capture data."
] | [
"Adaptation of an RL agent in a target environment with unknown dynamics is fast and safe when we transfer prior experience in a variety of environments and then select risk-averse actions during adaptation."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
The gap between the empirical success of deep learning and the lack of strong theoretical guarantees calls for studying simpler models. By observing that a ReLU neuron is a product of a linear function with a gate (the latter determines whether the neuron is active or not), where both share a jointly trained weight vec... | [
"We propose Gated Linear Unit networks — a model that performs similarly to ReLU networks on real data while being much easier to analyze theoretically."
] | [
"We introduce the NLC, a metric that is cheap to compute in the networks randomly initialized state and is highly predictive of generalization, at least in fully-connected networks."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Machine learning systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a different distribution from the one used for training. With their growing use in critical applications, it becomes important to develop systems that are able to accurately quantify its predictive uncer... | [
"We propose an architecture search method to identify a distribution of architectures and use it to construct a Bayesian ensemble for outlier detection."
] | [
"We propose a framework to learn confidence-calibrated networks by designing a novel loss function that incorporates predictive uncertainty estimated through stochastic inferences."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data. In this work we propose a novel image outlier detection approach (IOD for short) that leverages the cutting-edge image classifier to discover outliers without using any labeled outlier. We observe that although in... | [
"A novel approach that detects outliers from image data, while preserving the classification accuracy of image classification"
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources. We design a Dynamic Point-cloud Convolution (D-Conv) operator as the core component of CloudLSTMs, which performs convolution directly over point-clouds and ext... | [
"This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources."
] | [
"A control variate based stochastic training algorithm for graph convolutional networks that the receptive field can be only two neighbors per node."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge. For easy access to statistical approaches on relational data, multiple methods to embed a KG as components of R^d have been introduced. We propose TransINT, a novel and interpretable KG embedding method that iso... | [
"We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space in an explainable, robust, and geometrically coherent way."
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Unsupervised domain adaptive object detection aims to learn a robust detector on the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissi... | [
"We introduce a new gradient detach based complementary objective training strategy for domain adaptive object detection."
] | [
"Pseudo-labeling has shown to be a weak alternative for semi-supervised learning. We, conversely, demonstrate that dealing with confirmation bias with several regularizations makes pseudo-labeling a suitable approach."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Convolutional neural networks (CNN) have become the most successful and popular approach in many vision-related domains. While CNNs are particularly well-suited for capturing a proper hierarchy of concepts from real-world images, they are limited to domains where data is abundant. Recent attempts have looked into mitig... | [
"We propose a novel approach for connecting task-specific networks in a multi-task learning setting based on recent residual network advances."
] | [
"We find that deep networks which generalize poorly are more reliant on single directions than those that generalize well, and evaluate the impact of dropout and batch normalization, as well as class selectivity on single direction reliance."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Zero-Shot Learning (ZSL) is a classification task where some classes referred as unseen classes have no labeled training images. Instead, we only have side information (or description) about seen and unseen classes, often in the form of semantic or descriptive attributes. Lack of training images from a set of classes r... | [
"How to use cross-entropy loss for zero shot learning with soft labeling on unseen classes : a simple and effective solution that achieves state-of-the-art performance on five ZSL benchmark datasets."
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress. In this work, we use a curriculum of progressively growing action spaces to accelerate learning. We assume the environment is out of our control, but that the agent ... | [
"Progressively growing the available action space is a great curriculum for learning agents"
] | [
"We isolate one factor of RL generalization by analyzing the case when the agent only overfits to the observations. We show that architectural implicit regularizations occur in this regime."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Recently, researchers have discovered that the state-of-the-art object classifiers can be fooled easily by small perturbations in the input unnoticeable to human eyes. It is known that an attacker can generate strong adversarial examples if she knows the classifier parameters. Conversely, a defender can robustify the c... | [
"A game-theoretic solution to adversarial attacks and defenses."
] | [
"We propose a extension of the batch normalization, show a first-of-its-kind convergence analysis for this extension and show in numerical experiments that it has better performance than the original batch normalizatin."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Supervised learning with irregularly sampled time series have been a challenge to Machine Learning methods due to the obstacle of dealing with irregular time intervals. Some papers introduced recently recurrent neural network models that deals with irregularity, but most of them rely on complex mechanisms to achieve a ... | [
"A novel method to create dense descriptors of time (Time Embeddings) to make simple models understand temporal structures"
] | [
"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."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Community detection in graphs can be solved via spectral methods or posterior inference under certain probabilistic graphical models. Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational detection thresholds i... | [
"We propose a novel graph neural network architecture based on the non-backtracking matrix defined over the edge adjacencies and demonstrate its effectiveness in community detection tasks on graphs."
] | [
"This paper 1) characterizes functions representable by ReLU DNNs, 2) formally studies the benefit of depth in such architectures, 3) gives an algorithm to implement empirical risk minimization to global optimality for two layer ReLU nets."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Residual networks (Resnets) have become a prominent architecture in deep learning. However, a comprehensive understanding of Resnets is still a topic of ongoing research. A recent view argues that Resnets perform iterative refinement of features. We attempt to further expose properties of this aspect. To this end, we s... | [
"Residual connections really perform iterative inference"
] | [
"We find that deep networks which generalize poorly are more reliant on single directions than those that generalize well, and evaluate the impact of dropout and batch normalization, as well as class selectivity on single direction reliance."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
We develop end-to-end learned reconstructions for lensless mask-based cameras, including an experimental system for capturing aligned lensless and lensed images for training. Various reconstruction methods are explored, on a scale from classic iterative approaches (based on the physical imaging model) to deep learned m... | [
"We improve the reconstruction time and quality on an experimental mask-based lensless imager using an end-to-end learning approach which incorporates knowledge of the imaging model."
] | [
"We extend a state-of-the-art technique to directly incorporate FLOPs as part of the optimization objective, and we show that, given a desired FLOPs requirement, different neural networks are successfully trained."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years. With multiple hidden layers, deep learning models aim at computing the hierarchical feature representations of the observational data. Meanwhile, due to its severe disadvantages in data consumption, com... | [
"We introduce a new representation learning model, namely “Sample-Ensemble Genetic Evolutionary Network” (SEGEN), which can serve as an alternative approach to deep learning models."
] | [
"In this paper, we explore an internal dense yet external sparse network structure of deep neural networks and analyze its key properties."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
How can we teach artificial agents to use human language flexibly to solve problems in a real-world environment? We have one example in nature of agents being able to solve this problem: human babies eventually learn to use human language to solve problems, and they are taught with an adult human-in-the-loop. Unfortuna... | [
"We propose to use meta-learning for more efficient language learning, via a kind of 'domain randomization'. "
] | [
"Introduce an approach to allow agents to learn PPDDL action models incrementally over multiple planning problems under the framework of reinforcement learning."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization. This allows us to explicitly search for schedules that achieve good generalization. We describe the structure of the gradient of a validation error w.r.t. the learning rates, the hypergradient, and... | [
"MARTHE: a new method to fit task-specific learning rate schedules from the perspective of hyperparameter optimization"
] | [
"This paper 1) characterizes functions representable by ReLU DNNs, 2) formally studies the benefit of depth in such architectures, 3) gives an algorithm to implement empirical risk minimization to global optimality for two layer ReLU nets."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Recent years have witnessed some exciting developments in the domain of generating images from scene-based text descriptions. These approaches have primarily focused on generating images from a static text description and are limited to generating images in a single pass. They are unable to generate an image interactiv... | [
"Interactively generating image from incrementally growing scene graphs in multiple steps using GANs while preserving the contents of image generated in previous steps"
] | [
"Conditional VAE on top of latent spaces of pre-trained generative models that enables transfer between drastically different domains while preserving locality and semantic alignment."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images. However, most Convolutional Neural Networks (CNNs) for image classification were developed using biased datasets that contain large objects, in mostly central image posi... | [
"We study low- and very-low-signal-to-noise classification scenarios, where objects that correlate with class label occupy tiny proportion of the entire image (e.g. medical or hyperspectral imaging)."
] | [
"Deep neural networks trained with data augmentation do not require any other explicit regularization (such as weight decay and dropout) and exhibit greater adaptaibility to changes in the architecture and the amount of training data."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Recent trends of incorporating attention mechanisms in vision have led researchers to reconsider the supremacy of convolutional layers as a primary building block. Beyond helping CNNs to handle long-range dependencies, showed that attention can completely replace convolution and achieve state-of-the-art performance on ... | [
"A self-attention layer can perform convolution and often learns to do so in practice."
] | [
"We show shortcut connections should be placed in patterns that minimize between-layer distances during backpropagation, and design networks that achieve log L distances using L log(L) connections."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
We introduce a “learning-based” algorithm for the low-rank decomposition problem: given an $n \times d$ matrix $A$, and a parameter $k$, compute a rank-$k$ matrix $A'$ that minimizes the approximation loss $||A- A'||_F$. The algorithm uses a training set of input matrices in order to optimize its performance. Specifica... | [
"Learning-based algorithms can improve upon the performance of classical algorithms for the low-rank approximation problem while retaining the worst-case guarantee."
] | [
"We propose a extension of the batch normalization, show a first-of-its-kind convergence analysis for this extension and show in numerical experiments that it has better performance than the original batch normalizatin."
] | scitldr | {
"query": "Represent the Science paragraph:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Neural conversational models are widely used in applications like personal assistants and chat bots. These models seem to give better performance when operating on word level. However, for fusion languages like French, Russian and Polish vocabulary size sometimes become infeasible since most of the words have lots of w... | [
"Proposed architecture to solve morphological agreement task"
] | [
"We provide insightful understanding of sequence-labeling NER and propose to use two types of cross structures, both of which bring theoretical and empirical improvements."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
This paper proposes the use of spectral element methods \citep{canuto_spectral_1988} for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets; \citealp{Chen2018NeuralOD}) for system identification. This is achieved by expressing their dynamics as a truncated series of Legendre polynomials. The... | [
"This paper proposes the use of spectral element methods for fast and accurate training of Neural Ordinary Differential Equations for system identification."
] | [
"For complex constraints in which it is not easy to estimate the gradient, we use the discounted penalty as a guiding signal. We prove that under certain assumptions it converges to a feasible solution."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration. Commonly these signals are added as bonus rewards, which in ... | [
"A new intrinsic reward signal based on successor features and a novel way to combine extrinsic and intrinsic reward."
] | [
"Adaptation of an RL agent in a target environment with unknown dynamics is fast and safe when we transfer prior experience in a variety of environments and then select risk-averse actions during adaptation."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples. Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings. Existing approaches to finding efficient ... | [
"We propose to use a separate exploration policy to collect the pre-adaptation trajectories in MAML. We also show that using a self-supervised objective in the inner loop leads to more stable training and much better performance."
] | [
"We augment the Q-value estimates with a count-based bonus that ensures optimism during action selection and bootstrapping, even if the Q-value estimates are pessimistic."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
The “Supersymmetric Artificial Neural Network” in deep learning (denoted (x; θ, bar{θ})Tw), espouses the importance of considering biological constraints in the aim of further generalizing backward propagation. Looking at the progression of ‘solution geometries’; going from SO(n) representation (such as Perceptron like... | [
"Generalizing backward propagation, using formal methods from supersymmetry."
] | [
"We show that neural networks operate by changing topologly of a data set and explore how architectural choices effect this change."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Regularization-based continual learning approaches generally prevent catastrophic forgetting by augmenting the training loss with an auxiliary objective. However in most practical optimization scenarios with noisy data and/or gradients, it is possible that stochastic gradient descent can inadvertently change critical p... | [
"Regularizing the optimization trajectory with the Fisher information of old tasks reduces catastrophic forgetting greatly"
] | [
"We propose a hypothesis for why gradient descent generalizes based on how per-example gradients interact with each other."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
We study the problem of generating source code in a strongly typed, Java-like programming language, given a label (for example a set of API calls or types) carrying a small amount of information about the code that is desired. The generated programs are expected to respect a `"realistic" relationship between programs a... | [
"We give a method for generating type-safe programs in a Java-like language, given a small amount of syntactic information about the desired code."
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
We propose an approach for sequence modeling based on autoregressive normalizing flows. Each autoregressive transform, acting across time, serves as a moving reference frame for modeling higher-level dynamics. This technique provides a simple, general-purpose method for improving sequence modeling, with connections to ... | [
"We show how autoregressive flows can be used to improve sequential latent variable models."
] | [
"We propose a new class of inference models that iteratively encode gradients to estimate approximate posterior distributions."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs. This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks. We di... | [
"Adversarial examples can fool YouTube's copyright detection system"
] | [
"We can identify prototypical and outlier examples in machine learning that are quantifiably very different, and make use of them to improve many aspects of neural networks."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Equilibrium Propagation (EP) is a learning algorithm that bridges Machine Learning and Neuroscience, by computing gradients closely matching those of Backpropagation Through Time (BPTT), but with a learning rule local in space. Given an input x and associated target y, EP proceeds in two phases: in the first phase neur... | [
"We propose a continual version of Equilibrium Propagation, where neuron and synapse dynamics occur simultaneously throughout the second phase, with theoretical guarantees and numerical simulations."
] | [
"A theoretical analysis of a new class of RNNs, trained on neuroscience tasks, allows us to identify the role of dynamical dimensionality and cell classes in neural computations."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
There are two main lines of research on visual reasoning: neural module network (NMN) with explicit multi-hop reasoning through handcrafted neural modules, and monolithic network with implicit reasoning in the latent feature space. The former excels in interpretability and compositionality, while the latter usually ach... | [
"We propose a new Meta Module Network to resolve some of the restrictions of previous Neural Module Network to achieve strong performance on realistic visual reasoning dataset."
] | [
"Conditional VAE on top of latent spaces of pre-trained generative models that enables transfer between drastically different domains while preserving locality and semantic alignment."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
We propose a new perspective on adversarial attacks against deep reinforcement learning agents. Our main contribution is CopyCAT, a targeted attack able to consistently lure an agent into following an outsider's policy. It is pre-computed, therefore fast inferred, and could thus be usable in a real-time scenario. We sh... | [
"We propose a new attack for taking full control of neural policies in realistic settings."
] | [
"A backward model of previous (state, action) given the next state, i.e. P(s_t, a_t | s_{t+1}), can be used to simulate additional trajectories terminating at states of interest! Improves RL learning efficiency."
] | scitldr | {
"query": "Represent the Science article:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
Cold-start and efficiency issues of the Top-k recommendation are critical to large-scale recommender systems. Previous hybrid recommendation methods are effective to deal with the cold-start issues by extracting real latent factors of cold-start items(users) from side information, but they still suffer low efficiency i... | [
"It can generate effective hash codes for efficient cold-start recommendation and meanwhile provide a feasible marketing strategy."
] | [
"This work aims to provide quantitative answers to the relative importance of concepts of interest via concept activation vectors (CAV). In particular, this framework enables non-machine learning experts to express concepts of interest and test hypotheses using examples (e.g., a set of pictures that illustrate the... | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science abstract:",
"neg": "Represent the Science abstract:"
} |
Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems. In this paper, we propose a neural framework that can learn to solve the Circu... | [
"We propose a neural framework that can learn to solve the Circuit Satisfiability problem from (unlabeled) circuit instances."
] | [
"Are HMMs a special case of RNNs? We investigate a series of architectural transformations between HMMs and RNNs, both through theoretical derivations and empirical hybridization and provide new insights."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Sequence generation models such as recurrent networks can be trained with a diverse set of learning algorithms. For example, maximum likelihood learning is simple and efficient, yet suffers from the exposure bias problem. Reinforcement learning like policy gradient addresses the problem but can have prohibitively poor ... | [
"A unified perspective of various learning algorithms for sequence generation, such as MLE, RL, RAML, data noising, etc."
] | [
"We propose Dual Actor-Critic algorithm, which is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation. The algorithm achieves the state-of-the-art performances across several benchmarks."
] | scitldr | {
"query": "Represent the Science passage:",
"pos": "Represent the Science sentence:",
"neg": "Represent the Science sentence:"
} |
We are reporting the SHINRA project, a project for structuring Wikipedia with collaborative construction scheme. The goal of the project is to create a huge and well-structured knowledge base to be used in NLP applications, such as QA, Dialogue systems and explainable NLP systems. It is created based on a scheme of ”Re... | [
"We introduce a \"Resource by Collaborative Construction\" scheme to create KB, structured Wikipedia "
] | [
"The choice of the hub (target) language affects the quality of cross-lingual embeddings, which shouldn't be evaluated only on English-centric dictionaries."
] | scitldr | {
"query": "Represent the Science paper:",
"pos": "Represent the Science text:",
"neg": "Represent the Science text:"
} |
Recent image super-resolution(SR) studies leverage very deep convolutional neural networks and the rich hierarchical features they offered, which leads to better reconstruction performance than conventional methods. However, the small receptive fields in the up-sampling and reconstruction process of those models stop t... | [
"A state-of-the-art model based on global reasoning for image super-resolution"
] | [
"In this paper, we explore an internal dense yet external sparse network structure of deep neural networks and analyze its key properties."
] | scitldr | {
"query": "Represent the Science document:",
"pos": "Represent the Science summarization:",
"neg": "Represent the Science summarization:"
} |
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