paper_name stringlengths 11 170 | text stringlengths 8.07k 307k | summary stringlengths 152 6.16k | paper_id stringlengths 43 43 |
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Spike-based causal inference for weight alignment | 1 INTRODUCTION . Any learning system that makes small changes to its parameters will only improve if the changes are correlated to the gradient of the loss function . Given that people and animals can also show clear behavioral improvements on specific tasks ( Shadmehr et al. , 2010 ) , however the brain determines its... | This paper considers the "weight transport problem" which is the problem of ensuring that the feedforward weights $W_{ij}$ is the same as the feedback weights $W_{ji}$ in the spiking NN model of computation. This paper proposes a novel learning method for the feedback weights which depends on accurately estimating the ... | SP:76a052062e3e4bb707b24a8809c220c8ac1df83a |
Spike-based causal inference for weight alignment | 1 INTRODUCTION . Any learning system that makes small changes to its parameters will only improve if the changes are correlated to the gradient of the loss function . Given that people and animals can also show clear behavioral improvements on specific tasks ( Shadmehr et al. , 2010 ) , however the brain determines its... | Strong paper in the direction of a more biologically plausible solution for the weight transport problem, where the forward and the backward weights need to be aligned. Earlier work for feedback alignment has included methods such as hard-coding sign symmetry. In this method, the authors show that a piece-wise linear m... | SP:76a052062e3e4bb707b24a8809c220c8ac1df83a |
AdaGAN: Adaptive GAN for Many-to-Many Non-Parallel Voice Conversion | 1 INTRODUCTION . Language is the core of civilization , and speech is the most powerful and natural form of communication . Human voice mimicry has always been considered as one of the most difficult tasks since it involves understanding of the sophisticated human speech production mechanism ( Eriksson & Wretling ( 199... | This paper presents a voice conversion approach using GANs based on adaptive instance normalization (AdaIN). The authors give the mathematical formulation of the problem and provide the implementation of the so-called AdaGAN. Experiments are carried out on VCTK and the proposed AdaGAN is compared with StarGAN. The id... | SP:941824acd2bae699174e6bed954e2938eb4bede1 |
AdaGAN: Adaptive GAN for Many-to-Many Non-Parallel Voice Conversion | 1 INTRODUCTION . Language is the core of civilization , and speech is the most powerful and natural form of communication . Human voice mimicry has always been considered as one of the most difficult tasks since it involves understanding of the sophisticated human speech production mechanism ( Eriksson & Wretling ( 199... | This work describes an efficient voice conversion system that can operate on non-parallel samples and convert from and to multiple voices. The central element of the methodology is the AdaIn modification. This is an efficient speaker adaptive technique where features are re-normalized to a particular speaker's domain... | SP:941824acd2bae699174e6bed954e2938eb4bede1 |
Improving Evolutionary Strategies with Generative Neural Networks | 1 INTRODUCTION . We are interested in the global minimization of a black-box objective function , only accessible through a zeroth-order oracle . In many instances of this problem the objective is expensive to evaluate , which excludes brute force methods as a reasonable mean of optimization . Also , as the objective i... | In ES the goal is to find a distribution pi_theta(x) such that the expected value of f(x) under this distribution is high. This can be optimized with REINFORCE or with more sophisticated methods based on the natural gradient. The functional form of pi_theta is almost always a Gaussian, but this isn't sufficiently flexi... | SP:25106cb1a3e5ead20e58b680eeb6aa361c07e1ff |
Improving Evolutionary Strategies with Generative Neural Networks | 1 INTRODUCTION . We are interested in the global minimization of a black-box objective function , only accessible through a zeroth-order oracle . In many instances of this problem the objective is expensive to evaluate , which excludes brute force methods as a reasonable mean of optimization . Also , as the objective i... | As the title of the paper states, this paper tries to improve evolution strategies (ES) using a generative neural network. In the standard ES candidate solution is generated from a multivariate normal distribution, where the parameters of the distribution are adapted during the optimization process. The authors claim t... | SP:25106cb1a3e5ead20e58b680eeb6aa361c07e1ff |
Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models | 1 INTRODUCTION . Many of the generative models , for example , generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ; Arjovsky et al. , 2017 ; Salimans et al. , 2018 ) and normalizing flow models ( Rezende & Mohamed , 2015 ; Kingma & Dhariwal , 2018 ; Chen et al. , 2018 ) , aim to find a generator that c... | The paper proposes a ‘potential flow generator’ that can be seen as a regularizer for traditional GAN losses. It is based on the idea that samples flowing from one distribution to another should follow a minimum travel cost path. This regularization is expressed as an optimal transport problem with a squared Euclidean ... | SP:d6218fdd95b48f3e69bf12e96f938cecde8ff7ab |
Potential Flow Generator with $L_2$ Optimal Transport Regularity for Generative Models | 1 INTRODUCTION . Many of the generative models , for example , generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ; Arjovsky et al. , 2017 ; Salimans et al. , 2018 ) and normalizing flow models ( Rezende & Mohamed , 2015 ; Kingma & Dhariwal , 2018 ; Chen et al. , 2018 ) , aim to find a generator that c... | This is a great paper using optimal transport theory for generative and implicit models. Instead of using general vector fields, the authors apply the potential vector fields in optimal transport theory to design neural networks. The mathematics is correct with convincing examples. This brings an important mathematical... | SP:d6218fdd95b48f3e69bf12e96f938cecde8ff7ab |
Fast is better than free: Revisiting adversarial training | 1 INTRODUCTION . Although deep network architectures continue to be successful in a wide range of applications , the problem of learning robust deep networks remains an active area of research . In particular , safety and security focused applications are concerned about robustness to adversarial examples , data points... | The main claim of this paper is that a simple strategy of randomization plus fast gradient sign method (FGSM) adversarial training yields robust neural networks. This is somewhat surprising because previous works indicate that FGSM is not a powerful attack compared to iterative versions of it like projected gradient de... | SP:927a1f8069c0347c4d0a8b1b947533f1c508ba42 |
Fast is better than free: Revisiting adversarial training | 1 INTRODUCTION . Although deep network architectures continue to be successful in a wide range of applications , the problem of learning robust deep networks remains an active area of research . In particular , safety and security focused applications are concerned about robustness to adversarial examples , data points... | The authors claimed a classic adversarial training method, FGSM with random start, can indeed train a model that is robust to strong PGD attacks. Moreover, when it is combined with some fast training methods, such as cyclic learning rate scheduling and mixed precision, the adversarial training time can be significantl... | SP:927a1f8069c0347c4d0a8b1b947533f1c508ba42 |
BETANAS: Balanced Training and selective drop for Neural Architecture Search | Automatic neural architecture search techniques are becoming increasingly important in machine learning area . Especially , weight sharing methods have shown remarkable potentials on searching good network architectures with few computational resources . However , existing weight sharing methods mainly suffer limitatio... | This paper introduces a better searching strategy in the context of automatic neural architecture search (NAS). Especially, they focus on improving the search strategy for previously proposed computationally effective weight sharing methods for NAS. Current search strategies for the weight sharing NAS methods either fo... | SP:eb8b8a0bae8d3f488caf70b6103ed3fd9631cb9f |
BETANAS: Balanced Training and selective drop for Neural Architecture Search | Automatic neural architecture search techniques are becoming increasingly important in machine learning area . Especially , weight sharing methods have shown remarkable potentials on searching good network architectures with few computational resources . However , existing weight sharing methods mainly suffer limitatio... | In this paper, the authors proposed a new training strategy in achieving better balance between training efficiency and evaluation accuracy with weight sharing-based NAS algorithms. It is consisted of two phrases: in phrase 1, all path are uniformly trained to avoid bias, in phrase 2, less competitive options are prune... | SP:eb8b8a0bae8d3f488caf70b6103ed3fd9631cb9f |
Iterative energy-based projection on a normal data manifold for anomaly localization | 1 INTRODUCTION . Automating visual inspection on production lines with artificial intelligence has gained popularity and interest in recent years . Indeed , the analysis of images to segment potential manufacturing defects seems well suited to computer vision algorithms . However these solutions remain data hungry and ... | The paper proposes to use autoencoder for anomaly localization. The approach learns to project anomalous data on an autoencoder-learned manifold by using gradient descent on energy derived from the autoencoder's loss function. The proposed method is evaluated using the anomaly-localization dataset (Bergmann et al. CVPR... | SP:1f95868a91ef213ebf3be6ca2a0f059e93b4be37 |
Iterative energy-based projection on a normal data manifold for anomaly localization | 1 INTRODUCTION . Automating visual inspection on production lines with artificial intelligence has gained popularity and interest in recent years . Indeed , the analysis of images to segment potential manufacturing defects seems well suited to computer vision algorithms . However these solutions remain data hungry and ... | This paper discusses an important problem of solving the visual inspection problem limited supervision. It proposes to use VAE to model the anomaly detection. The major concern is how the quality of f_{VAE} is estimated. From the paper it seems f_{VAE} is not updated. Will it be sufficient to rely a fixed f_{VAE} and ... | SP:1f95868a91ef213ebf3be6ca2a0f059e93b4be37 |
Long History Short-Term Memory for Long-Term Video Prediction | While video prediction approaches have advanced considerably in recent years , learning to predict long-term future is challenging — ambiguous future or error propagation over time yield blurry predictions . To address this challenge , existing algorithms rely on extra supervision ( e.g. , action or object pose ) , mot... | The paper proposes a type of recurrent neural network module called Long History Short-Term Memory (LH-STM) for longer-term video generation. This module can be used to replace ConvLSTMs in previously published video prediction models. It expands ConvLSTMs by adding a "previous history" term to the ConvLSTM equations t... | SP:cf0db5624fc03cd71e331202c16808174b4a9ae7 |
Long History Short-Term Memory for Long-Term Video Prediction | While video prediction approaches have advanced considerably in recent years , learning to predict long-term future is challenging — ambiguous future or error propagation over time yield blurry predictions . To address this challenge , existing algorithms rely on extra supervision ( e.g. , action or object pose ) , mot... | This paper proposes a new LSTM architecture called LH-STM (and Double LH-STM). The main idea deals with having a history selection mechanism to directly extract what information from the past. The authors also propose to decompose the history and update in LH-STM into two networks called Double LH-STM. In experiments, ... | SP:cf0db5624fc03cd71e331202c16808174b4a9ae7 |
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View | 1 INTRODUCTION . The Transformer is one of the most commonly used neural network architectures in natural language processing . Variants of the Transformer have achieved state-of-the-art performance in many tasks including language modeling ( Dai et al. , 2019 ; Al-Rfou et al. , 2018 ) and machine translation ( Vaswani... | In this work, the authors show that the sequence of self-attention and feed-forward layers within a Transformer can be interpreted as an approximate numerical solution to a set of coupled ODEs. Based on this insight, the authors propose to replace the first-order Lie-Trotter splitting scheme by the more accurate, secon... | SP:69da1cecdf9fc25a9e6263943a5396b606cdcfef |
Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View | 1 INTRODUCTION . The Transformer is one of the most commonly used neural network architectures in natural language processing . Variants of the Transformer have achieved state-of-the-art performance in many tasks including language modeling ( Dai et al. , 2019 ; Al-Rfou et al. , 2018 ) and machine translation ( Vaswani... | The paper points out a formal analogy between transformers and an ODE modelling multi-particle convection (the feed-forward network) and diffusion (the self-attention head). The paper then adapts the Strang-Marchuk splitting scheme for solving ODEs to construct a slightly different transformer architecture: “FFN of Att... | SP:69da1cecdf9fc25a9e6263943a5396b606cdcfef |
Risk Averse Value Expansion for Sample Efficient and Robust Policy Learning | 1 INTRODUCTION . In contrast to the tremendous progress made by model-free reinforcement learning algorithms in the domain of games ( Mnih et al. , 2015 ; Silver et al. , 2017 ; Vinyals et al. , 2019 ) , poor sample efficiency has risen up as a great challenge to RL , especially when interacting with the real world . T... | This paper proposes a novel deep reinforcement learning algorithm at the intersection of model-based and model-free reinforcement learning: Risk Averse Value Expansion (RAVE). Overall, this work represents a significant but incremental step forwards for this "hybrid"-RL class of algorithms. However, the paper itself ha... | SP:fc98effb95b87ad325f609c31b336c7dafd9ac30 |
Risk Averse Value Expansion for Sample Efficient and Robust Policy Learning | 1 INTRODUCTION . In contrast to the tremendous progress made by model-free reinforcement learning algorithms in the domain of games ( Mnih et al. , 2015 ; Silver et al. , 2017 ; Vinyals et al. , 2019 ) , poor sample efficiency has risen up as a great challenge to RL , especially when interacting with the real world . T... | This paper expands on previous work on hybrid model-based and model-free reinforcement learning. Specifically, it expands on the ideas in Model-based Value Expansion (MVE) and Stochastic Ensemble Value Expansion (STEVE) with a dynamically-scaled variance bias term to increase risk aversion over the course of learning, ... | SP:fc98effb95b87ad325f609c31b336c7dafd9ac30 |
Encoding Musical Style with Transformer Autoencoders | 1 INTRODUCTION . There has been significant progress in generative modeling , particularly with respect to creative applications such as art and music ( Oord et al. , 2016 ; Engel et al. , 2017b ; Ha & Eck , 2017 ; Huang et al. , 2019a ; Payne , 2019 ) . As the number of generative applications increase , it becomes in... | This paper presents a technique for encoding the high level “style” of pieces of symbolic music. The music is represented as a variant of the MIDI format. The main strategy is to condition a Music Transformer architecture on this global “style embedding”. Additionally, the Music Transformer model is also conditioned o... | SP:bddd3d499426725b02d3d67ca0a7f8ef0c30e639 |
Encoding Musical Style with Transformer Autoencoders | 1 INTRODUCTION . There has been significant progress in generative modeling , particularly with respect to creative applications such as art and music ( Oord et al. , 2016 ; Engel et al. , 2017b ; Ha & Eck , 2017 ; Huang et al. , 2019a ; Payne , 2019 ) . As the number of generative applications increase , it becomes in... | In this paper, the author extends the standard music Transformer into a conditional version: two encoders are evolved, one for encoding the performance and the other is used for encoding the melody. The output representation has to be similar to the input. The authors conduct experiments on the MAESTRO dataset and an i... | SP:bddd3d499426725b02d3d67ca0a7f8ef0c30e639 |
Corpus Based Amharic Sentiment Lexicon Generation | keywords : Amharic Sentiment lexicon , Amharic Sentiment Classification , Seed words 1 INTRODUCTION . Most of sentiment mining research papers are associated to English languages . Linguistic computational resources in languages other than English are limited . Amharic is one of resource limited languages . Due to the ... | This paper introduces a corpus-based approach to build sentiment lexicon for Amharic. In order to save time and costs for the resource-limited language, the lexicon is generated from an Amharic news corpus by the following steps: manually preparing polarized seed words lists (strongly positive and strongly negative), c... | SP:e472738b53eec7967504021365ac5b4808028ec1 |
Corpus Based Amharic Sentiment Lexicon Generation | keywords : Amharic Sentiment lexicon , Amharic Sentiment Classification , Seed words 1 INTRODUCTION . Most of sentiment mining research papers are associated to English languages . Linguistic computational resources in languages other than English are limited . Amharic is one of resource limited languages . Due to the ... | This paper proposes a domain-specific corpus-based approach for generating semantic lexicons for the low-resource Amharic language. Manual construction of lexicons is especially hard and expensive for low-resource languages. More importantly, the paper points out that existing dictionaries and lexicons do not capture c... | SP:e472738b53eec7967504021365ac5b4808028ec1 |
Quantum Semi-Supervised Kernel Learning | Quantum machine learning methods have the potential to facilitate learning using extremely large datasets . While the availability of data for training machine learning models is steadily increasing , oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels . One of the approaches... | The paper proposes a quantum computer-based algorithm for semi-supervised least squared kernel SVM. This work builds upon LS-SVM of Rebentrost et al (2014b) which developed a quantum algorithm for the supervised version of the problem. While the main selling point of quantum LS-SVM is that it scales logarithmically wit... | SP:77d59e1e726172184249bdfdd81011617dc9c208 |
Quantum Semi-Supervised Kernel Learning | Quantum machine learning methods have the potential to facilitate learning using extremely large datasets . While the availability of data for training machine learning models is steadily increasing , oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels . One of the approaches... | This paper developes a quantum algorithm for kernel-based support vector machine working in a semi-supervised learning setting. The motivation is to utilise the significant advantage of quantum computation to train machine learning models on large-scale datasets efficiently. This paper reviews the existing work on usin... | SP:77d59e1e726172184249bdfdd81011617dc9c208 |
Invertible generative models for inverse problems: mitigating representation error and dataset bias | 1 INTRODUCTION . Generative deep neural networks have shown remarkable performance as natural signal priors in imaging inverse problems , such as denoising , inpainting , compressed sensing , blind deconvolution , and phase retrieval . These generative models can be trained from datasets consisting of images of particu... | This paper proposes to employ the likelihood of the latent representation of images as the optimization target in the Glow (Kingma and Dhariwal, 2018) framework. The authors argue that to optimize the ''proxy for image likelihood'' has two advantages: First, the landscapes of the surface are more smooth; Second, a late... | SP:e58dc2d21175a62499405b7f4c3a03b135530838 |
Invertible generative models for inverse problems: mitigating representation error and dataset bias | 1 INTRODUCTION . Generative deep neural networks have shown remarkable performance as natural signal priors in imaging inverse problems , such as denoising , inpainting , compressed sensing , blind deconvolution , and phase retrieval . These generative models can be trained from datasets consisting of images of particu... | This paper investigates the performance of invertible generative models for solving inverse problems. They argue that their most significant benefit over GAN priors is the lack of representation error that (1) enables invertible models to perform well on out-of-distribution data and (2) results in a model that does not... | SP:e58dc2d21175a62499405b7f4c3a03b135530838 |
Deep symbolic regression | 1 INTRODUCTION . Understanding the mathematical relationships among variables in a physical system is an integral component of the scientific process . Symbolic regression aims to identify these relationships by searching over the space of tractable mathematical expressions to best fit a dataset . Specifically , given ... | This paper presents deep symbolic regression (DSR), which uses a recurrent neural network to learn a distribution over mathematical expressions and uses policy gradient to train the RNN for generating desired expressions given a set of points. The RNN model is used to sample expressions from the learned distribution, w... | SP:0d872fb4321f3a4a3fc61cf4d33b0c7e33f2d695 |
Deep symbolic regression | 1 INTRODUCTION . Understanding the mathematical relationships among variables in a physical system is an integral component of the scientific process . Symbolic regression aims to identify these relationships by searching over the space of tractable mathematical expressions to best fit a dataset . Specifically , given ... | This paper presents a RNN-RL based method for the symbolic regression problem. The problem is new (to Deep RL) and interesting. My main concern is about the proposed method, where the three RL related equations (not numbered) at page 5 are also direct copy-from-textbook policy gradient equations without specific adapta... | SP:0d872fb4321f3a4a3fc61cf4d33b0c7e33f2d695 |
A NEW POINTWISE CONVOLUTION IN DEEP NEURAL NETWORKS THROUGH EXTREMELY FAST AND NON PARAMETRIC TRANSFORMS | 1 INTRODUCTION . Large Convolutional Neural Networks ( CNNs ) ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2014 ; He et al. , 2016 ; Szegedy et al. , 2016b ; a ) and automatic Neural Architecture Search ( NAS ) based networks ( Zoph et al. , 2018 ; Liu et al. , 2018 ; Real et al. , 2018 ) have evolved to show re... | This paper proposes a new pointwise convolution layer, which is non-parametric and can be efficient thanks to the fast conventional transforms. Specifically, it could use either DCT or DHWT to do the transforming job and explores the optimal block structure to use this new kind of PC layer. Extensive experimental studi... | SP:4706017e6f8b958c7d0825fed98b285ea2994b59 |
A NEW POINTWISE CONVOLUTION IN DEEP NEURAL NETWORKS THROUGH EXTREMELY FAST AND NON PARAMETRIC TRANSFORMS | 1 INTRODUCTION . Large Convolutional Neural Networks ( CNNs ) ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2014 ; He et al. , 2016 ; Szegedy et al. , 2016b ; a ) and automatic Neural Architecture Search ( NAS ) based networks ( Zoph et al. , 2018 ; Liu et al. , 2018 ; Real et al. , 2018 ) have evolved to show re... | This paper presents a new pointwise convolution (PC) method which applies conventional transforms such as DWHT and DCT. The proposed method aims to reduce the computational complexity of CNNs without degrading the performance. Compared with the original PC layer, the DWHT/DCT-based methods do not require any learnable ... | SP:4706017e6f8b958c7d0825fed98b285ea2994b59 |
Uncertainty-Aware Prediction for Graph Neural Networks | 1 INTRODUCTION . Inherent uncertainties introduced by different root causes have emerged as serious hurdles to find effective solutions for real world problems . Critical safety concerns have been brought due to lack of considering diverse causes of uncertainties , resulting in high risk due to misinterpretation of unc... | This paper proposes to model various uncertainty measures in Graph Convolutional Networks (GCN) by Bayesian MC Dropout. Compared to existing Bayesian GCN methods, this work stands out in two aspects: 1) in terms of prediction, it considers multiple uncertainty measures including aleatoric, epistemic, vacuity and disson... | SP:63ad3be1dae7ede5c02a847304072c1cbc91b1cb |
Uncertainty-Aware Prediction for Graph Neural Networks | 1 INTRODUCTION . Inherent uncertainties introduced by different root causes have emerged as serious hurdles to find effective solutions for real world problems . Critical safety concerns have been brought due to lack of considering diverse causes of uncertainties , resulting in high risk due to misinterpretation of unc... | The authors proposed a Bayesian graph neural network framework for node classification. The proposed models outperformed the baselines in six node classification tasks. The main contribution is to evaluate various uncertainty measures for the uncertainty analysis of Bayesian graph neural networks. The authors show that... | SP:63ad3be1dae7ede5c02a847304072c1cbc91b1cb |
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