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Demystifying How Self-Supervised Features Improve Training from Noisy Labels | 1 INTRODUCTION . Deep Neural Networks ( DNNs ) have achieved remarkable performance in many areas including speech recognition ( Graves et al. , 2013 ) , computer vision ( Krizhevsky et al. , 2012 ; Lotter et al. , 2016 ) , natural language processing ( Zhang & LeCun , 2015 ) etc . The high-achieving performance often ... | This paper studies how self-supervised pre-training impacts the resistance of the neural network to noisy labels. The paper provides both theoretical analyses and numerical experiments of their study. The main contribution of the provided study is (i) given a quality encoder pre-trained from SSL, a simple linear layer... | SP:1a4a9d9e7677e56afd88997ab0da2d64303b499c |
Demystifying How Self-Supervised Features Improve Training from Noisy Labels | 1 INTRODUCTION . Deep Neural Networks ( DNNs ) have achieved remarkable performance in many areas including speech recognition ( Graves et al. , 2013 ) , computer vision ( Krizhevsky et al. , 2012 ; Lotter et al. , 2016 ) , natural language processing ( Zhang & LeCun , 2015 ) etc . The high-achieving performance often ... | The authors illustrate why self-supervised learning can help learning in training with noisy labels. The main contributions are: 1. The authors illustrate theoretically why learning good representation can help learning with noisy labels 2. The authors describe why fixed encoders are important. 3. The authors motivate ... | SP:1a4a9d9e7677e56afd88997ab0da2d64303b499c |
Demystifying How Self-Supervised Features Improve Training from Noisy Labels | 1 INTRODUCTION . Deep Neural Networks ( DNNs ) have achieved remarkable performance in many areas including speech recognition ( Graves et al. , 2013 ) , computer vision ( Krizhevsky et al. , 2012 ; Lotter et al. , 2016 ) , natural language processing ( Zhang & LeCun , 2015 ) etc . The high-achieving performance often ... | This paper studies the usefulness of self-supervised features when encountering data with noisy labels. It presents an array of ideas on this topic: including the question of fine-tuning pre-trained representations, using ideas from distillation as regularisation to improve generalisation. Some theoretical and empirica... | SP:1a4a9d9e7677e56afd88997ab0da2d64303b499c |
Spending Your Winning Lottery Better After Drawing It | Lottery Ticket Hypothesis ( LTH ) ( Frankle & Carbin , 2019 ) suggests that a dense neural network contains a sparse sub-network that can match the performance of the original dense network when trained in isolation from scratch . Most works retrain the sparse sub-network with the same training protocols as its dense n... | The authors propose several "tweaks" to sparse re-training of lottery tickets that have been identified by iterative magnitude pruning (IMP). These tweaks include 1) the replacement of ReLU activation functions by smooth activation functions, 2) the use of soft-labels instead of one-hot labels, 3) learned layer-wise ... | SP:0f65fcb656c7ea471f50c8d3f6851f8c2c2f5ed9 |
Spending Your Winning Lottery Better After Drawing It | Lottery Ticket Hypothesis ( LTH ) ( Frankle & Carbin , 2019 ) suggests that a dense neural network contains a sparse sub-network that can match the performance of the original dense network when trained in isolation from scratch . Most works retrain the sparse sub-network with the same training protocols as its dense n... | The authors propose that the training and structure of models found by lottery tickets rewinding (LTR)/lottery tickets (LT) should be modified from that of the dense model, unlike what was typically done in at least the original LT paper, and propose a set of training and architectural changes to improve retraining of ... | SP:0f65fcb656c7ea471f50c8d3f6851f8c2c2f5ed9 |
Spending Your Winning Lottery Better After Drawing It | Lottery Ticket Hypothesis ( LTH ) ( Frankle & Carbin , 2019 ) suggests that a dense neural network contains a sparse sub-network that can match the performance of the original dense network when trained in isolation from scratch . Most works retrain the sparse sub-network with the same training protocols as its dense n... | The paper aims to improve Lottery Ticket Hypothesis (LTH). The paper shows that the combination of several techniques, such as different activations, skip connection, and knowledge distillation, can improve the performance of the sparse sub-network obtained from dense networks. The paper validates the method on some ne... | SP:0f65fcb656c7ea471f50c8d3f6851f8c2c2f5ed9 |
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization | 1 INTRODUCTION . Deep Learning ( DL ) is the backbone of Artificial Intelligence as a Service ( AIaaS ) ( Ribeiro et al. , 2015 ) , which is being provided in a wide range of applications including music composition ( Briot et al. , 2020 ) , autonomous driving ( Li et al. , 2021a ) , smart building ( Xu et al. , 2020a ... | In the era of deep learning, pre-trained models have been regarded as intellectual properties of AI companies. Thus, protecting these models has been more and more important. To achieve this aim, this paper proposes a non-transferable learning (NTL) method to capture the exclusive data representation in the learned mod... | SP:fc3be226cf3cdf2821f45e68c4a800d1337c7abc |
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization | 1 INTRODUCTION . Deep Learning ( DL ) is the backbone of Artificial Intelligence as a Service ( AIaaS ) ( Ribeiro et al. , 2015 ) , which is being provided in a wide range of applications including music composition ( Briot et al. , 2020 ) , autonomous driving ( Li et al. , 2021a ) , smart building ( Xu et al. , 2020a ... | This paper introduces the idea of "non-transferable learning", which is roughly what the name indicates. The authors explain the value of this as a security/IP protection tool to protect the model from being used on unauthorized data. In addition, this presents a kind of attack against domain adaption works that try to... | SP:fc3be226cf3cdf2821f45e68c4a800d1337c7abc |
Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization | 1 INTRODUCTION . Deep Learning ( DL ) is the backbone of Artificial Intelligence as a Service ( AIaaS ) ( Ribeiro et al. , 2015 ) , which is being provided in a wide range of applications including music composition ( Briot et al. , 2020 ) , autonomous driving ( Li et al. , 2021a ) , smart building ( Xu et al. , 2020a ... | Protecting the intellectual property of the trained models has received appealing attentions. Existing researches to protect intellectual property fall into two major categories: ownership verification and usage authorization. To this end, the authors propose to utilize non-transferable learning to achieve both the go... | SP:fc3be226cf3cdf2821f45e68c4a800d1337c7abc |
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training | 1 INTRODUCTION . Transformers have recently been applied to various vision tasks , including image classification ( Liu et al. , 2021 ; Dong et al. , 2021 ; Bao et al. , 2021 ) , object detection ( Carion et al. , 2020 ; Zhu et al. , 2020 ) , semantic segmentation ( Xie et al. , 2021 ; Cheng et al. , 2021 ) , video und... | This work presents the gradient conflict issue in ViT training, i.e., the gradients of sub-networks conflict with that of the supernet, leads to inferior performance of ViT supernet training. The authors fix this issue by 1) a gradient project method to prioritize the sub-network update; 2) Use switchable layers to inc... | SP:41877cb2a05a7fdb6123f904778317f105db911b |
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training | 1 INTRODUCTION . Transformers have recently been applied to various vision tasks , including image classification ( Liu et al. , 2021 ; Dong et al. , 2021 ; Bao et al. , 2021 ) , object detection ( Carion et al. , 2020 ; Zhu et al. , 2020 ) , semantic segmentation ( Xie et al. , 2021 ; Cheng et al. , 2021 ) , video und... | The paper proposes a NAS algorithm for vision transformers. The search space contains both CNN blocks and transformer blocks. The authors solve the gradient conflict dilemma in this process and find out a good architecture. Experiments on classification and segmentation show its effectiveness over other vison transfor... | SP:41877cb2a05a7fdb6123f904778317f105db911b |
NASViT: Neural Architecture Search for Efficient Vision Transformers with Gradient Conflict aware Supernet Training | 1 INTRODUCTION . Transformers have recently been applied to various vision tasks , including image classification ( Liu et al. , 2021 ; Dong et al. , 2021 ; Bao et al. , 2021 ) , object detection ( Carion et al. , 2020 ; Zhu et al. , 2020 ) , semantic segmentation ( Xie et al. , 2021 ; Cheng et al. , 2021 ) , video und... | This paper aims at applying one-shot Neural Architecture Search (NAS) to Vision Transformers (ViTs). The authors claim that directly using existing CNN based NAS method to ViTs will lead to a gradient conflict issue. In order to tackle this issue, the authors propose three techniques, including a gradient projection, a... | SP:41877cb2a05a7fdb6123f904778317f105db911b |
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data | 1 INTRODUCTION . Conditional image synthesis , also X2I translation , maps from an input domain ( e.g . text , audio , segmentation maps , etc . ) to the image domain . Benefiting from GANs ( Goodfellow et al. , 2014 ) and its follow-up improved versions ( Gulrajani et al. , 2017 ; Kang & Park , 2020 ; Salimans et al. ... | The work introduces a flexible method to distill the knowledge of unconditional GANs of images to various image translation tasks, including image-to-image, text-to-image, and audio-to-image translation. While prior work requires using the same architecture of the pre-trained GANs to train the downstream im2im tasks, t... | SP:d59e17faaba144ea4e55c1d77849bc21c19eeff7 |
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data | 1 INTRODUCTION . Conditional image synthesis , also X2I translation , maps from an input domain ( e.g . text , audio , segmentation maps , etc . ) to the image domain . Benefiting from GANs ( Goodfellow et al. , 2014 ) and its follow-up improved versions ( Gulrajani et al. , 2017 ; Kang & Park , 2020 ; Salimans et al. ... | In this paper, the authors present a unifield learning method to investigate the knowledge transfer for X2I translation. Compared to existing methods, there are two advantages here: (1) this framework can be used for varying kinds of conditional image synthesis tasks; (2) it relieves the limitation for student generato... | SP:d59e17faaba144ea4e55c1d77849bc21c19eeff7 |
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data | 1 INTRODUCTION . Conditional image synthesis , also X2I translation , maps from an input domain ( e.g . text , audio , segmentation maps , etc . ) to the image domain . Benefiting from GANs ( Goodfellow et al. , 2014 ) and its follow-up improved versions ( Gulrajani et al. , 2017 ; Kang & Park , 2020 ; Salimans et al. ... | The paper presents a framework for knowledge distillation in image-to-image translation and arbitrary domain-to-image (e.g., text-to-image) translation. Authors claim following contributions: (1) a unified transfer learning method that leverages only synthetic images, (2) a semantic diversity loss, (3) a style-mixing t... | SP:d59e17faaba144ea4e55c1d77849bc21c19eeff7 |
Sample-efficient actor-critic algorithms with an etiquette for zero-sum Markov games | We introduce algorithms based on natural actor-critic and analyze their sample complexity for solving two player zero-sum Markov games in the tabular case . Our results improve the best-known sample complexities of policy gradient/actorcritic methods for convergence to Nash equilibrium in the multi-agent setting . We u... | This paper considers algorithms based on natural actor-critic for solving two player zero-sum Markov games in the tabular case. In particular, the authors focus on the analysis of the sample complexity for a two-stage algorithm that solves a matrix game and a single agent problem, alternatively and iteratively. By com... | SP:f460a21378458bd0576233891e977f66c010ae62 |
Sample-efficient actor-critic algorithms with an etiquette for zero-sum Markov games | We introduce algorithms based on natural actor-critic and analyze their sample complexity for solving two player zero-sum Markov games in the tabular case . Our results improve the best-known sample complexities of policy gradient/actorcritic methods for convergence to Nash equilibrium in the multi-agent setting . We u... | This paper studies the sample complexity of learning algorithms in two-player zero-sum tabular Markov games. The authors propose a two-stage algorithm that requires agents to agree on an etiquette, which means the agents have behave differently in two stages. Based on two assumptions on stationary state distribution an... | SP:f460a21378458bd0576233891e977f66c010ae62 |
Sample-efficient actor-critic algorithms with an etiquette for zero-sum Markov games | We introduce algorithms based on natural actor-critic and analyze their sample complexity for solving two player zero-sum Markov games in the tabular case . Our results improve the best-known sample complexities of policy gradient/actorcritic methods for convergence to Nash equilibrium in the multi-agent setting . We u... | This work proposes a natural actor-critic (NAC) algorithm for two-player zero-sum Markov game in the tabular case. The algorithm is model-free, private, but asymmetric, and the convergence guarantee is established in terms of the expected one-sided Nash-equilibrium duality gap. Among the existing policy-based algorithm... | SP:f460a21378458bd0576233891e977f66c010ae62 |
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method | 1 INTRODUCTION . As a major value function evaluation method , temporal difference ( TD ) learning ( Sutton , 1988 ; Dayan , 1992 ) has been widely used in various planning problems in reinforcement learning . Although TD learning performs successfully in the on-policy settings , where an agent can interact with enviro... | This paper proposed a new off-policy evaluation successor method of ETD. The method has reduced variance by leveraging a simple and effective way that restarting follow-on trace iteration every couple of updates. The authors also provide theoretical analysis that shows that the proposed method improves the converge rat... | SP:8b914a5ce2dae75515370f788c99b04039218fd4 |
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method | 1 INTRODUCTION . As a major value function evaluation method , temporal difference ( TD ) learning ( Sutton , 1988 ; Dayan , 1992 ) has been widely used in various planning problems in reinforcement learning . Although TD learning performs successfully in the on-policy settings , where an agent can interact with enviro... | The authors propose an improved variant of emphatic temporal difference (ETD) aimed at addressing issues of high variance when faced with a large mismatch between behavior and target policies. The main improvement of PER-ETD, the proposed algorithm, comes from periodically clearing the follow-on trace at logarthmically... | SP:8b914a5ce2dae75515370f788c99b04039218fd4 |
PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method | 1 INTRODUCTION . As a major value function evaluation method , temporal difference ( TD ) learning ( Sutton , 1988 ; Dayan , 1992 ) has been widely used in various planning problems in reinforcement learning . Although TD learning performs successfully in the on-policy settings , where an agent can interact with enviro... | This paper presents a technique of reducing the variance of emphatic algorithms by resetting the trace periodically, and that increasing the period logarithmicaly can result in an optimal way of trading off bias and variance of the resulting learned value function. The authors prove such a method obtains the optimal tr... | SP:8b914a5ce2dae75515370f788c99b04039218fd4 |
Evaluating generative networks using Gaussian mixtures of image features | 1 INTRODUCTION . Generative networks , such as generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014a ) and variational autoencoders ( Kingma & Welling , 2013 ) , model distributions implicitly by trying to learn a map from a simple distribution , such as a Gaussian , to the desired target distribution . ... | This paper generalizes the widely-used FID metric for image generation evaluation by fitting a mixture of Gaussians instead of a single Gaussian on the extracted features. The advantage of the proposed approach is it removes the unrealistic assumption of FID that the extracted features from some encoder networks (such ... | SP:397daca667c4f4f2413dd716989d9c5cb4df90a8 |
Evaluating generative networks using Gaussian mixtures of image features | 1 INTRODUCTION . Generative networks , such as generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014a ) and variational autoencoders ( Kingma & Welling , 2013 ) , model distributions implicitly by trying to learn a map from a simple distribution , such as a Gaussian , to the desired target distribution . ... | This paper propose a new method to evaluate GANs. Current prevailing evaluation of GANs is FID, which has one assumption that the evaluated data (or feature) has Gaussian distribution. However, this assumption is false when practically applying it. Inspired by recent work MW$_2$, authors propose GMMs to evaluate tw... | SP:397daca667c4f4f2413dd716989d9c5cb4df90a8 |
Evaluating generative networks using Gaussian mixtures of image features | 1 INTRODUCTION . Generative networks , such as generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014a ) and variational autoencoders ( Kingma & Welling , 2013 ) , model distributions implicitly by trying to learn a map from a simple distribution , such as a Gaussian , to the desired target distribution . ... | This paper targets to a new evaluation metric for the performance of generative models given a set of real images and a set of fake images. The authors show that Inception-v3 features of the imageNet dataset are not Gaussian and remedy this issue by modeling image features using Gaussian mixture models (GMMs) and formu... | SP:397daca667c4f4f2413dd716989d9c5cb4df90a8 |
On the Latent Holes 🧀 of VAEs for Text Generation | 1 INTRODUCTION . Variational Auto-Encoders ( VAEs ) are powerful unsupervised models for learning low-dimensional manifolds ( aka . a latent space ) from non-trivial high-dimensional data ( Kingma & Welling , 2014 ; Rezende et al. , 2014 ) . They have found successes in a number of downstream tasks across different app... | This paper focuses on the discontinuities (aka. holes) in the latent space of VAE. Unlike previous work which concentrates on the encoder side, this paper pays attention to the decoder network who plays an important role in generation tasks. This paper analyzes two existing latent hole indicators and proves that they c... | SP:4c020f0efc7031e0850de38213a56b52ca7f6343 |
On the Latent Holes 🧀 of VAEs for Text Generation | 1 INTRODUCTION . Variational Auto-Encoders ( VAEs ) are powerful unsupervised models for learning low-dimensional manifolds ( aka . a latent space ) from non-trivial high-dimensional data ( Kingma & Welling , 2014 ; Rezende et al. , 2014 ) . They have found successes in a number of downstream tasks across different app... | The paper is concerned with discontinuities in the latent space of Variational Autoencoders, specifically with finding those so called holes algorithmically. There are three main contributions: * A theoretical analysis of two existing algorithms for finding holes. The result shows that one is strictly more powerful tha... | SP:4c020f0efc7031e0850de38213a56b52ca7f6343 |
On the Latent Holes 🧀 of VAEs for Text Generation | 1 INTRODUCTION . Variational Auto-Encoders ( VAEs ) are powerful unsupervised models for learning low-dimensional manifolds ( aka . a latent space ) from non-trivial high-dimensional data ( Kingma & Welling , 2014 ; Rezende et al. , 2014 ) . They have found successes in a number of downstream tasks across different app... | This paper studies the holes within the latent space of text VAEs. The major contribution is a hole detection algorithm, which firstly projects the latent representations to a principle subspace, then performs tree-based BFS to detect holes. However, this paper suffers from its unclear presentation of the algorithm (wi... | SP:4c020f0efc7031e0850de38213a56b52ca7f6343 |
Physics-Informed Neural Operator for Learning Partial Differential Equations | 1 INTRODUCTION . Machine learning-based methods are starting to show promise in scientific computing and especially in solving partial differential equations ( PDEs ) . They have demonstrated advantages in both efficiency and accuracy compared to conventional solvers . They are even able to tackle previously intractabl... | The paper proposes the physics-informed neural operator (PINO). It combines the operating-learning and function-optimization frameworks, which improves convergence rates and accuracy over traditional methods. Experiments test the advantage of PINO. | SP:460930c4b1d73c6c5a7c03d5e14b987afcde8c2b |
Physics-Informed Neural Operator for Learning Partial Differential Equations | 1 INTRODUCTION . Machine learning-based methods are starting to show promise in scientific computing and especially in solving partial differential equations ( PDEs ) . They have demonstrated advantages in both efficiency and accuracy compared to conventional solvers . They are even able to tackle previously intractabl... | The authors introduce physics-informed neural operator for learning partial differential equations (PDEs) from data with deep learning. This approach combines two recent and popular methods in the field of machine learning and PDEs, the physics-informed neural network (PINNs) for solving PDEs and Fourier neural operato... | SP:460930c4b1d73c6c5a7c03d5e14b987afcde8c2b |
Physics-Informed Neural Operator for Learning Partial Differential Equations | 1 INTRODUCTION . Machine learning-based methods are starting to show promise in scientific computing and especially in solving partial differential equations ( PDEs ) . They have demonstrated advantages in both efficiency and accuracy compared to conventional solvers . They are even able to tackle previously intractabl... | This paper proposes Physics-Informed Neural Operator (PINO) by combining two previous methods, Fourier neural operators (FNO) and physics-informed neural networks (PINNs). Similar to one use case of FNO, PINO learns the solution operator over multiple instances of a parametric PDE family in the first phase. In the seco... | SP:460930c4b1d73c6c5a7c03d5e14b987afcde8c2b |
Rethinking Adversarial Transferability from a Data Distribution Perspective | 1 INTRODUCTION . Deep neural networks ( DNNs ) are widely used in various safety-critical fields , but they are vulnerable to adversarial examples ( Szegedy et al. , 2013 ) . Adversarial attacks are imperceptible to humans but catastrophic for the DNNs and can be transferred between different models ( Goodfellow et al.... | This paper presents a new adversarial attack method which could generate adversarial examples with higher transferability. The proposed method is based on the observation that low-density region of the training data is not well trained. To utilize this, the authors try to align the adversarial direction with the direct... | SP:1e8c197e57285e0fc7e05a458f8bf1513aff7a47 |
Rethinking Adversarial Transferability from a Data Distribution Perspective | 1 INTRODUCTION . Deep neural networks ( DNNs ) are widely used in various safety-critical fields , but they are vulnerable to adversarial examples ( Szegedy et al. , 2013 ) . Adversarial attacks are imperceptible to humans but catastrophic for the DNNs and can be transferred between different models ( Goodfellow et al.... | This paper identifies that adversarial examples in the low-density region of the groud truth distribution have much stronger transferability. This observation leads to the AAI metric which evaluate the alignment of the model’s adversarial attack with intrinsic attack direction. The paper further identifies a set of mod... | SP:1e8c197e57285e0fc7e05a458f8bf1513aff7a47 |
Rethinking Adversarial Transferability from a Data Distribution Perspective | 1 INTRODUCTION . Deep neural networks ( DNNs ) are widely used in various safety-critical fields , but they are vulnerable to adversarial examples ( Szegedy et al. , 2013 ) . Adversarial attacks are imperceptible to humans but catastrophic for the DNNs and can be transferred between different models ( Goodfellow et al.... | This paper proposes Intrinsic Adversarial Attack (IAA), a transfer attack method based by jointly matching data distribution. The key assumption of this paper is that the DNN might not be well trained on low-density regions (LDD). Therefore, taking data distribution into consideration during attack could potentially im... | SP:1e8c197e57285e0fc7e05a458f8bf1513aff7a47 |
Dynamic Least-Squares Regression | 1 INTRODUCTION . The problem of least-squares regression ( LSR ) dates back to Gauss in 1821 ( Stigler , 1981 ) , and is the backbone of statistical inference ( Hastie et al. , 2001 ) , signal processing ( Rabiner & Gold , 1975 ) , convex optimization ( Bubeck , 2015 ) , control theory ( Chui , 1990 ) and network routi... | This paper studies incremental least-squares regression, where the goal is to maintain an $(1+\epsilon)$-approximate solution to $\min_x \left\Vert Ax-b\right\Vert_2^2$ for some $A\in\mathbb{R}^{n\times d}$, under row insertions to $\begin{pmatrix}A & b\end{pmatrix}$, while keeping the total runtime as low as possible.... | SP:0d10eaac6836b37a301cbbe5213610a0a5d1e18c |
Dynamic Least-Squares Regression | 1 INTRODUCTION . The problem of least-squares regression ( LSR ) dates back to Gauss in 1821 ( Stigler , 1981 ) , and is the backbone of statistical inference ( Hastie et al. , 2001 ) , signal processing ( Rabiner & Gold , 1975 ) , convex optimization ( Bubeck , 2015 ) , control theory ( Chui , 1990 ) and network routi... | The paper considers solving the regression problem min_x |Ax-b|_2 in the online setting, where A in R^{n x d} and b in R^n are given row by row, one at each time. The main task is to maintain a good approximate solution x (meaning that |Ax-b|_2 <= (1+eps)*OPT) throughout this process, with the update time as small as p... | SP:0d10eaac6836b37a301cbbe5213610a0a5d1e18c |
Dynamic Least-Squares Regression | 1 INTRODUCTION . The problem of least-squares regression ( LSR ) dates back to Gauss in 1821 ( Stigler , 1981 ) , and is the backbone of statistical inference ( Hastie et al. , 2001 ) , signal processing ( Rabiner & Gold , 1975 ) , convex optimization ( Bubeck , 2015 ) , control theory ( Chui , 1990 ) and network routi... | The paper discusses _Dynamic Least Squares_: the problem where the rows of a overdetermined least squares problem are revealed incrementally, and an algorithm has to maintain an accurate solution to the least squares system as these rows are revealed. In prior work, this problem has been studied in the context of spac... | SP:0d10eaac6836b37a301cbbe5213610a0a5d1e18c |
StyleNeRF: A Style-based 3D Aware Generator for High-resolution Image Synthesis | 1 INTRODUCTION . Photo-realistic free-view image synthesis of real-world scenes is a long-standing problem in computer vision and computer graphics . Traditional graphics pipeline requires production-quality 3D models , computationally expensive rendering , and manual work , making it challenging to apply to large-scal... | In this manuscript, authors proposed a novel 3D-aware generative model for photo-realistic high resolution image synthesis. The proposed method combines Neural Radiance Fields (NerF) and StyleGAN, and tackles the challenges of efficiency, multi-view consistency and rendering quality. Specifically, authors propose to us... | SP:121fa4d034d563334ba12c898c706377957fabad |
StyleNeRF: A Style-based 3D Aware Generator for High-resolution Image Synthesis | 1 INTRODUCTION . Photo-realistic free-view image synthesis of real-world scenes is a long-standing problem in computer vision and computer graphics . Traditional graphics pipeline requires production-quality 3D models , computationally expensive rendering , and manual work , making it challenging to apply to large-scal... | The paper presented StyleNeRF, a 3D-aware generative model for high-resolution image synthesis with high multi-view consistency. StyleNeRF integrates the neural radiance field (NeRF) into a style-based generator to improve rendering efficiency and 3D consistency. It performs volume rendering only to produce a low-res... | SP:121fa4d034d563334ba12c898c706377957fabad |
StyleNeRF: A Style-based 3D Aware Generator for High-resolution Image Synthesis | 1 INTRODUCTION . Photo-realistic free-view image synthesis of real-world scenes is a long-standing problem in computer vision and computer graphics . Traditional graphics pipeline requires production-quality 3D models , computationally expensive rendering , and manual work , making it challenging to apply to large-scal... | This paper proposes StyleNeRF which combines NeRF and a style-based generator to improve rendering efficiency and 3D consistency in high-resolution image synthesis. In this paper, (1) The NeRF is used to produce a low-resolution feature map and upsample it progressively to high resolution. (2) Several designs are propo... | SP:121fa4d034d563334ba12c898c706377957fabad |
Improved Generalization Bound for Deep Neural Networks Using Geometric Functional Analysis | 1 INTRODUCTION . The problem of generalization of deep neural networks from the perspective of theoretical analysis has recently received a considerable amount of interest ( Neyshabur et al. , 2015 ; Zhang et al. , 2017 ; Dziugaite & Roy , 2017 ; Neyshabur et al. , 2018 ; Golowich et al. , 2018 ; Nagarajan & Kolter , 2... | The paper considers the problem of obtaining generalization bounds for deep neural networks. The paper uses tools from geometric function analysis to derive bounds for the covering number of neural networks, and derive generalization bounds using the covering numbers. The main strength of the bound as claimed by the pa... | SP:bacb5a7dd0580560f255a67e73d15baa92d7934c |
Improved Generalization Bound for Deep Neural Networks Using Geometric Functional Analysis | 1 INTRODUCTION . The problem of generalization of deep neural networks from the perspective of theoretical analysis has recently received a considerable amount of interest ( Neyshabur et al. , 2015 ; Zhang et al. , 2017 ; Dziugaite & Roy , 2017 ; Neyshabur et al. , 2018 ; Golowich et al. , 2018 ; Nagarajan & Kolter , 2... | This paper aims at proving generalization bound via geometric functional analysis. Although the topic is interesting, this paper suffers from an unclear organization and there are some unclear points in the proof (see the following for more details). Therefore, I tend to give a "reject" and I hope the authors can provi... | SP:bacb5a7dd0580560f255a67e73d15baa92d7934c |
Improved Generalization Bound for Deep Neural Networks Using Geometric Functional Analysis | 1 INTRODUCTION . The problem of generalization of deep neural networks from the perspective of theoretical analysis has recently received a considerable amount of interest ( Neyshabur et al. , 2015 ; Zhang et al. , 2017 ; Dziugaite & Roy , 2017 ; Neyshabur et al. , 2018 ; Golowich et al. , 2018 ; Nagarajan & Kolter , 2... | The authors study the generalization bound of neural networks. First, They assume that the activation functions as polynomials with certain degrees. Under this assumption, they prove a novel generalization bound using geometric functional analysis. The authors also compared their bounds with the method in (Neyshabur et... | SP:bacb5a7dd0580560f255a67e73d15baa92d7934c |
The Low-Rank Simplicity Bias in Deep Networks | 1 INTRODUCTION . It has become conventional wisdom that the more layers one adds , the better a deep neural network ( DNN ) performs . This guideline is supported , in part , by theoretical results showing that deeper networks can require far fewer parameters than shallower networks to obtain the same modeling “ capaci... | This paper conduct several controlled experiments and arrive at the following results: - Deep networks (both linear and non-linear) are biased towards learning low-rank embeddings at initialization. - This low-rank bias exists even after the training is done regardless of the initialization or training algorithm. This... | SP:97b02d8f1a4adf5a8690c326d832c4ed614f07ea |
The Low-Rank Simplicity Bias in Deep Networks | 1 INTRODUCTION . It has become conventional wisdom that the more layers one adds , the better a deep neural network ( DNN ) performs . This guideline is supported , in part , by theoretical results showing that deeper networks can require far fewer parameters than shallower networks to obtain the same modeling “ capaci... | This work proposes the hypothesis that deeper networks are inductively biased to find low-rank solutions (i.e., in the sense of gram matrix of features being low-rank) and presents empirical evidence to support the same. The authors thoroughly show that this inductive bias is a property of the parameterization of neura... | SP:97b02d8f1a4adf5a8690c326d832c4ed614f07ea |
The Low-Rank Simplicity Bias in Deep Networks | 1 INTRODUCTION . It has become conventional wisdom that the more layers one adds , the better a deep neural network ( DNN ) performs . This guideline is supported , in part , by theoretical results showing that deeper networks can require far fewer parameters than shallower networks to obtain the same modeling “ capaci... | The paper explores the effect of depth on a continuous estimate of rank (effective rank) of the data embeddings computed by feed-forward neural networks. On artificial datasets, the paper shows that at initialization (with common uniform or normal weight distributions), the effective rank of both linear and non-linear... | SP:97b02d8f1a4adf5a8690c326d832c4ed614f07ea |
Open Set Domain Adaptation with Zero-shot Learning on Graph | 1 INTRODUCTION . In the last decades , deep learning models have shown good performance in various tasks , especially in visual perception . The training of the deep learning network relies on plenty of labeled data . However , most of the existing large labeled datasets are collected from the Internet . The images in ... | The paper formulates a novel problem in open-set domain adaptation and aims to classify the unknown classes in the target domain. This is different from the traditional open-set domain adaptation problem setting. To do this, additional knowledge of inter-class relations are employed and embedded so that knowledge learn... | SP:a9d638e2afc2e3a87c18c146e52d1351cfe73918 |
Open Set Domain Adaptation with Zero-shot Learning on Graph | 1 INTRODUCTION . In the last decades , deep learning models have shown good performance in various tasks , especially in visual perception . The training of the deep learning network relies on plenty of labeled data . However , most of the existing large labeled datasets are collected from the Internet . The images in ... | They propose a new setting in open-set domain adaptation, where the goal is to classify known classes into their classes as well as cluster unknown classes well. A difference from an existing open-set domain adaptation is that it does only require separating unknown instances from known ones whereas this paper aims to... | SP:a9d638e2afc2e3a87c18c146e52d1351cfe73918 |
Open Set Domain Adaptation with Zero-shot Learning on Graph | 1 INTRODUCTION . In the last decades , deep learning models have shown good performance in various tasks , especially in visual perception . The training of the deep learning network relies on plenty of labeled data . However , most of the existing large labeled datasets are collected from the Internet . The images in ... | The paper considers the problem of open-set domain adaptation where the target domain has additional group of unknown classes and domain-shift with source domain. One interesting aspect of the paper is that the method utilizes a Knowledge Graph for zero-shot learning on the unknown classes. Further, the method utilizes... | SP:a9d638e2afc2e3a87c18c146e52d1351cfe73918 |
Acceleration of Federated Learning with Alleviated Forgetting in Local Training | 1 INTRODUCTION . Federated learning ( FL ) has emerged as a paradigm to train a global machine learning model in a distributed manner while taking privacy concerns and data protection regulations into consideration by keeping data on clients ( Voigt & Von dem Bussche , 2017 ) . The main challenge faced in FL is how to ... | The authors claim that in non-iid federated learning setups models experience catastrophic forgetting of previously encountered data. As a result, the performance of the model degrades and the convergence is slower. As a solution, the authors propose FedReg, a method to regularize the model with pseudo-data generated f... | SP:9a7447196730e3d0003272aaa1c29b8ae21d9aad |
Acceleration of Federated Learning with Alleviated Forgetting in Local Training | 1 INTRODUCTION . Federated learning ( FL ) has emerged as a paradigm to train a global machine learning model in a distributed manner while taking privacy concerns and data protection regulations into consideration by keeping data on clients ( Voigt & Von dem Bussche , 2017 ) . The main challenge faced in FL is how to ... | This paper aims to alleviate forgetting in federated learning on non iid data. The method proposed FedReg focuses on regularizing locally trained parameters with the loss on generated pseudo data, which are based on adversarial examples of the global model in the previous step and the adversarial examples of the local ... | SP:9a7447196730e3d0003272aaa1c29b8ae21d9aad |
Acceleration of Federated Learning with Alleviated Forgetting in Local Training | 1 INTRODUCTION . Federated learning ( FL ) has emerged as a paradigm to train a global machine learning model in a distributed manner while taking privacy concerns and data protection regulations into consideration by keeping data on clients ( Voigt & Von dem Bussche , 2017 ) . The main challenge faced in FL is how to ... | This paper considers the catastrophic forgetting issue in federated learning. The authors observe that this issue is (at least partially) responsible for slow convergence of existing FL methods when the data are not independently and identically distributed (non-i.i.d.) across different clients. This paper proposes Fed... | SP:9a7447196730e3d0003272aaa1c29b8ae21d9aad |
Label-Efficient Semantic Segmentation with Diffusion Models | 1 INTRODUCTION . Denoising diffusion probabilistic models ( DDPM ) ( Sohl-Dickstein et al. , 2015 ; Ho et al. , 2020 ) have recently outperformed alternative approaches to model the distribution of natural images both in the realism of individual samples and their diversity ( Dhariwal & Nichol , 2021 ) . These advantag... | This paper explores to what extent Denoising Diffusion Probabilistic Models (DDPMs) serve as good representational learners for transfer or semi-supervised learning on downstream tasks. They are particularly interested in the semantic segmentation as a prototypical dense computer vision task. They find that DDPM do pr... | SP:7b1e5eed0da0d5d3c3618f7c848ae79e9e96dffe |
Label-Efficient Semantic Segmentation with Diffusion Models | 1 INTRODUCTION . Denoising diffusion probabilistic models ( DDPM ) ( Sohl-Dickstein et al. , 2015 ; Ho et al. , 2020 ) have recently outperformed alternative approaches to model the distribution of natural images both in the realism of individual samples and their diversity ( Dhariwal & Nichol , 2021 ) . These advantag... | The paper demonstrates that the intermediate activations in Denoising diffusion probabilistic models (DDPM) can capture semantic information and thus can serve as representation for high-level vision tasks. The paper provides an interesting analysis of how well each layer at each diffusion step in DDPM can serve as a r... | SP:7b1e5eed0da0d5d3c3618f7c848ae79e9e96dffe |
Label-Efficient Semantic Segmentation with Diffusion Models | 1 INTRODUCTION . Denoising diffusion probabilistic models ( DDPM ) ( Sohl-Dickstein et al. , 2015 ; Ho et al. , 2020 ) have recently outperformed alternative approaches to model the distribution of natural images both in the realism of individual samples and their diversity ( Dhariwal & Nichol , 2021 ) . These advantag... | This paper proposes to treat deep activations of denoising diffusion probabilistic models trained on image datasets as unsupervised pixel features. To extract features for an image, one performs a fixed number of steps of the diffusion process, passes the resulting noisy image through the U-Net denoiser, and upsamples ... | SP:7b1e5eed0da0d5d3c3618f7c848ae79e9e96dffe |
Conditional GANs with Auxiliary Discriminative Classifier | 1 INTRODUCTION . Generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) have been gained great progress in learning high-dimensional , complex data distribution such as natural images ( Karras et al. , 2019 ; 2020b ; a ; Brock et al. , 2019 ) . Standard GANs consist of a generator network that transfers ... | This paper proposes the Auxiliary Discriminative Classifier GAN (ADC-GAN) to eliminate a contractionary objective and conditional entropy in ACGAN generator training. Specifically, the authors mathematically demonstrate that training ACGAN without a discriminative label classifier causes minimizing an undesirable diver... | SP:f81454b460aac0ede0a0c9a238c8d4cd51396ee5 |
Conditional GANs with Auxiliary Discriminative Classifier | 1 INTRODUCTION . Generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) have been gained great progress in learning high-dimensional , complex data distribution such as natural images ( Karras et al. , 2019 ; 2020b ; a ; Brock et al. , 2019 ) . Standard GANs consist of a generator network that transfers ... | - This paper aims to solve the low intra-class diversity on generated images of AC-GAN, a classifier-based cGAN. - As far as I know, this is an important issue that limits classifier-based cGANs (the counterpart is the projection-based cGAN, i.e, PD-GAN). - The authors point out that the reason is that the classifier ... | SP:f81454b460aac0ede0a0c9a238c8d4cd51396ee5 |
Conditional GANs with Auxiliary Discriminative Classifier | 1 INTRODUCTION . Generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) have been gained great progress in learning high-dimensional , complex data distribution such as natural images ( Karras et al. , 2019 ; 2020b ; a ; Brock et al. , 2019 ) . Standard GANs consist of a generator network that transfers ... | The paper is about improving conditional GANs. To be specifically, it also aims to resolve the bias issue of ACGAN by proposing a discriminative classifier. The discriminative classifier is a hybrid model of discriminator and classifier, where it has to not tell real or fake, but also the class. Preliminary analysis o... | SP:f81454b460aac0ede0a0c9a238c8d4cd51396ee5 |
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals | 1 Introduction . The interest in using deep learning for EEG related tasks has been rapidly growing in the last years , specially for applications in sleep staging , seizure detection and prediction , and brain-computer interfaces ( BCI ) ( Roy et al. , 2019 ) . Data augmentation is a well-known regularization techniqu... | The paper proposes an automatic differentiable data augmentation algorithm for EEG data that outperforms existing methods. They also propose novel augmentations for EEG that help the model to train better in low-labeled data regimes. They also show preliminary results showcasing that class-wise augmentation can be bett... | SP:19d0d6d651f83f8f0eeeae61357264bae7e1b204 |
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals | 1 Introduction . The interest in using deep learning for EEG related tasks has been rapidly growing in the last years , specially for applications in sleep staging , seizure detection and prediction , and brain-computer interfaces ( BCI ) ( Roy et al. , 2019 ) . Data augmentation is a well-known regularization techniqu... | THis paper proposes a special version of AutoAugment to search class-wise data augmentation policies for EEG data. The main contribution of this paper is a novel differentiable relaxation algorithm on EEG data (ADDA) that significantly efficiency of policy search. Through the EEG sleep staging task, the paper shows the... | SP:19d0d6d651f83f8f0eeeae61357264bae7e1b204 |
CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals | 1 Introduction . The interest in using deep learning for EEG related tasks has been rapidly growing in the last years , specially for applications in sleep staging , seizure detection and prediction , and brain-computer interfaces ( BCI ) ( Roy et al. , 2019 ) . Data augmentation is a well-known regularization techniqu... | This paper studies gradient-based automatic data augmentation algorithms amenable to class-wise policies with exponentially larger search spaces. It presents a method, called CADDA, to address the problem of automatic data augmentation with application on EEG signals. The proposed method achieves good results for EEG ... | SP:19d0d6d651f83f8f0eeeae61357264bae7e1b204 |
Language Model Pre-training Improves Generalization in Policy Learning | 1 INTRODUCTION . In recent years , language models ( LMs ) trained on open-domain text corpora have come to play a central role in machine learning approaches to natural language processing tasks ( Devlin et al. , 2018 ) . This includes tasks that are not purely linguistic , and additionally require nontrivial planning... | **After rebuttal**: I am keeping my score. But I will not fight against rejecting the paper. I think the results are promising but the scope of the experiments are limited and the claims need to be more precise. As pointed out in my discussion with the authors, there are also several important missing details that make... | SP:6982fbcb6e7f4e90d89cfdd2cb2e0b5d0bbd9f1d |
Language Model Pre-training Improves Generalization in Policy Learning | 1 INTRODUCTION . In recent years , language models ( LMs ) trained on open-domain text corpora have come to play a central role in machine learning approaches to natural language processing tasks ( Devlin et al. , 2018 ) . This includes tasks that are not purely linguistic , and additionally require nontrivial planning... | This paper investigates the effectiveness of the language model for training the policy in embodied environments. The authors use a pre-trained GPT-2 to initialize the policy, then show the generalization effect in policy learning. In the experiments, the authors demonstrated the language model shows a better generaliz... | SP:6982fbcb6e7f4e90d89cfdd2cb2e0b5d0bbd9f1d |
Language Model Pre-training Improves Generalization in Policy Learning | 1 INTRODUCTION . In recent years , language models ( LMs ) trained on open-domain text corpora have come to play a central role in machine learning approaches to natural language processing tasks ( Devlin et al. , 2018 ) . This includes tasks that are not purely linguistic , and additionally require nontrivial planning... | This paper takes a transformer-based language model, pre-trained on a large text corpus (In this case GPT-2) and uses it for the symbolic version of the VirtualHome environment. The observation, goals and action history of the agent are encoded as text strings in a few various ways and fed as input to the transformer a... | SP:6982fbcb6e7f4e90d89cfdd2cb2e0b5d0bbd9f1d |
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach | 1 INTRODUCTION . Automatic music composition usages can vary from continuous generation of copyright free music for use in media to inspiration tools for musicians . With recent advances of neural networks and with the availability of large set of big data on digitized music scores composed by humans , the trend of aut... | This paper presents a method for polyphonic piano-based symbolic music generation based on previous work on RL-tuned recurrent networks. This paper adds an adversarial inverse reinforcement learning (AIRL) step to estimate a reward function which is used in tandem to music theoretic rewards during the Q-network tuning.... | SP:d27bf1100a4d0adfedcf4291827f2b94c25db69a |
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach | 1 INTRODUCTION . Automatic music composition usages can vary from continuous generation of copyright free music for use in media to inspiration tools for musicians . With recent advances of neural networks and with the availability of large set of big data on digitized music scores composed by humans , the trend of aut... | This work proposes an approach to polyphonic music composition based on reinforcement learning. The approach constructs a reward function based on inverse reinforcement learning (IRL) using human demonstrations, in conjunction with a hand-crafted, music-theoretic reward. Empirical evaluation compares the approach based... | SP:d27bf1100a4d0adfedcf4291827f2b94c25db69a |
Polyphonic Music Composition: An Adversarial Inverse Reinforcement Learning Approach | 1 INTRODUCTION . Automatic music composition usages can vary from continuous generation of copyright free music for use in media to inspiration tools for musicians . With recent advances of neural networks and with the availability of large set of big data on digitized music scores composed by humans , the trend of aut... | This paper introduces a novel pipeline for generating polyphonic music. To generate music, the authors first pretrained a bi-axial LSTM (in time and pitch dimensions) on a corpus of piano rolls. To finetune the model’s performance, the authors then viewed music generation as an RL task and further trained their LSTM-ba... | SP:d27bf1100a4d0adfedcf4291827f2b94c25db69a |
Variational Component Decoder for Source Extraction from Nonlinear Mixture | 1 INTRODUCTION . Signal extraction from nonlinear mixture is a recurring yet hard research problem in signal processing and representation learning . To tackle this problem , conventional methods can be divided into two categories . One is Blind Source Separation ( BSS ) that recovers all sources in the mixture ( Comon... | The paper proposes an approach for supervised non-linear regression for multivariate time-series using a sequence-to-sequence approach with self-attention and generative prior on the latent codes. The paper poses this as a source extraction from a nonlinear mixture. The paper shows how this can be applied to a synthet... | SP:af2d296d9fd547aaa5906380d556faf2ef47e997 |
Variational Component Decoder for Source Extraction from Nonlinear Mixture | 1 INTRODUCTION . Signal extraction from nonlinear mixture is a recurring yet hard research problem in signal processing and representation learning . To tackle this problem , conventional methods can be divided into two categories . One is Blind Source Separation ( BSS ) that recovers all sources in the mixture ( Comon... | Authors present a novel approach that uses deep learning to solve non-linear Blind Source Separation problems. More specifically, their approach combines Seq2Seq and variational inference to extract one source of interest out of the nonlinear mixture. The generative model incorporates the prior beliefs about the source... | SP:af2d296d9fd547aaa5906380d556faf2ef47e997 |
Variational Component Decoder for Source Extraction from Nonlinear Mixture | 1 INTRODUCTION . Signal extraction from nonlinear mixture is a recurring yet hard research problem in signal processing and representation learning . To tackle this problem , conventional methods can be divided into two categories . One is Blind Source Separation ( BSS ) that recovers all sources in the mixture ( Comon... | Authors propose a supervised variational component decoder (sVCD) framework that estimates a single source signal sequence from non-linear mixtures. Proposed model relies on a sequence-to-sequence translating variational encoder-decoder architecture, where optimization is performed based on a variational lower bound on... | SP:af2d296d9fd547aaa5906380d556faf2ef47e997 |
Enforcing physics-based algebraic constraints for inference of PDE models on unstructured grids | 1 INTRODUCTION . Multiple works have shown the capability of neural networks to solve complex physical problems and learn the behavior of physical systems from data . Examples include learning and solving ordinary differential equations ( ODEs ) [ 6 ] , partial differential equations ( PDEs ) [ 28 ; 20 ] and rigid body... | The authors present a method to incorporate constraints into the output of learnable PDE models. They cover point-wise, differential, and integral constraints. They achieve this by representing the PDE solution in a basis, as is common for the variational method (e.g. pseudo-spectral method, and finite element method).... | SP:b3aff23abb861090b004f9d33436b69961421cf9 |
Enforcing physics-based algebraic constraints for inference of PDE models on unstructured grids | 1 INTRODUCTION . Multiple works have shown the capability of neural networks to solve complex physical problems and learn the behavior of physical systems from data . Examples include learning and solving ordinary differential equations ( ODEs ) [ 6 ] , partial differential equations ( PDEs ) [ 28 ; 20 ] and rigid body... | The paper proposes a two-folded method to enforce constraints of different natures (differential, integrals….) on a statistical model learned from physical data. The constraints are not enforced directly on the model but rather on “interpolant” functions that aims at completing the original model in between the observe... | SP:b3aff23abb861090b004f9d33436b69961421cf9 |
Enforcing physics-based algebraic constraints for inference of PDE models on unstructured grids | 1 INTRODUCTION . Multiple works have shown the capability of neural networks to solve complex physical problems and learn the behavior of physical systems from data . Examples include learning and solving ordinary differential equations ( ODEs ) [ 6 ] , partial differential equations ( PDEs ) [ 28 ; 20 ] and rigid body... | This paper proposes a method to enforce physical constraints in deep learning models. It provides a nice summary of local, differential and integral constraints, and frames them in a Lagrangian setting. For a reason which I could not clearly follow, the paper focuses on GAN early on. This is intuitive to me, as the phy... | SP:b3aff23abb861090b004f9d33436b69961421cf9 |
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data | 1 INTRODUCTION . Imitation learning uses expert demonstration data to learn sequential decision making policies ( Schaal , 1999 ) . Such demonstrations , often produced by human experts , can be costly to obtain in large number . On the other hand , practical application domains , such as recommendation ( Afsar et al. ... | The paper considers an imitation learning (IL) problem with both expert and suboptimal demonstrations. The paper claims that sub-optimal demonstrations can be used to learn latent action abstractions which can improve the efficiency of down-stream IL. To solve this problem, the paper proposes TRAIL, which pre-trains an... | SP:5d92df04ac10df3f758eb86ec74fdca197fa25a3 |
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data | 1 INTRODUCTION . Imitation learning uses expert demonstration data to learn sequential decision making policies ( Schaal , 1999 ) . Such demonstrations , often produced by human experts , can be costly to obtain in large number . On the other hand , practical application domains , such as recommendation ( Afsar et al. ... | The paper proposes an imitation learning algorithm, TRAIL, that can benefit from a large amount of suboptimal demonstrations besides a small amount of high-quality demonstrations. This is achieved through learning a factored transition model with action reparameterization from the suboptimal or even random demonstratio... | SP:5d92df04ac10df3f758eb86ec74fdca197fa25a3 |
TRAIL: Near-Optimal Imitation Learning with Suboptimal Data | 1 INTRODUCTION . Imitation learning uses expert demonstration data to learn sequential decision making policies ( Schaal , 1999 ) . Such demonstrations , often produced by human experts , can be costly to obtain in large number . On the other hand , practical application domains , such as recommendation ( Afsar et al. ... | The paper proposes a method to accelerate behavioral cloning (BC) (especially in the low data regime) by utilizing a (much larger) auxiliary dataset of suboptimal behaviors. The authors claim that learning a good latent action representation (in this case, by learning a transition-based action representation from the ... | SP:5d92df04ac10df3f758eb86ec74fdca197fa25a3 |
Semi-supervised Long-tailed Recognition using Alternate Sampling | 1 INTRODUCTION . Large-scale datasets , which contain sufficient data in each class , has been a major factor to the success of modern deep learning models for computer vision tasks , such as object recognition . These datasets are usually carefully curated and balanced to have an uniform data distribution over all cla... | This paper proposes a new setting--semi-supervised long-tailed recognition. To harness the imbalanced unlabeled data, the authors combined the decoupling in long-tailed recognition and pseudo-labeling in semi-supervised learning, which formulates a three-stage method. Stage 1 generates pseudo-labels with a classifier t... | SP:935238eca487fe47d7539e122c692b639f8d2966 |
Semi-supervised Long-tailed Recognition using Alternate Sampling | 1 INTRODUCTION . Large-scale datasets , which contain sufficient data in each class , has been a major factor to the success of modern deep learning models for computer vision tasks , such as object recognition . These datasets are usually carefully curated and balanced to have an uniform data distribution over all cla... | This paper proposes a long-tailed semi-supervised learning setting where both labeled and unlabeled data from a long-tailed distribution exist. To address this setting, they use an iterative decoupling training method, which is based on Kang et al 2020. The only difference is after initialization using the original dec... | SP:935238eca487fe47d7539e122c692b639f8d2966 |
Semi-supervised Long-tailed Recognition using Alternate Sampling | 1 INTRODUCTION . Large-scale datasets , which contain sufficient data in each class , has been a major factor to the success of modern deep learning models for computer vision tasks , such as object recognition . These datasets are usually carefully curated and balanced to have an uniform data distribution over all cla... | This paper propose a new setting, named semi-supervised long-tailed recognition. They consider both of the labelled data and unlabelled data exhibit long-tailed distribution. To solve the problem, they propose an alternate sampling framework which learns the feature and classifier separately and update them iteratively... | SP:935238eca487fe47d7539e122c692b639f8d2966 |
Unconditional Diffusion Guidance | 1 INTRODUCTION . Diffusion models have recently emerged as an expressive and flexible family of generative models , delivering competitive sample quality and likelihood scores on image and audio synthesis tasks ( SohlDickstein et al. , 2015 ; Song & Ermon , 2019 ; Ho et al. , 2020 ; Song et al. , 2021b ; Kingma et al. ... | In this paper the authors propose an improvement for score-matching based generative modeling [1] resembling low temperature sampling as in GANs or flow-based models. Similarly to [2] they propose to modify the drift function used in the sampling step of the diffusion model by including the gradient of some classifier.... | SP:5e647ea6f857d222ed538db72d17e7d806b2acde |
Unconditional Diffusion Guidance | 1 INTRODUCTION . Diffusion models have recently emerged as an expressive and flexible family of generative models , delivering competitive sample quality and likelihood scores on image and audio synthesis tasks ( SohlDickstein et al. , 2015 ; Song & Ermon , 2019 ; Ho et al. , 2020 ; Song et al. , 2021b ; Kingma et al. ... | This work proposed a method to trade-off sample diversity for sample quality in diffusion models, which is termed unconditional guidance. Different from the prior work called classifier guidance (Dhariwal & Nichol 2021) that relies on a classifier for providing the guidance signal, the proposed unconditional guidance m... | SP:5e647ea6f857d222ed538db72d17e7d806b2acde |
Unconditional Diffusion Guidance | 1 INTRODUCTION . Diffusion models have recently emerged as an expressive and flexible family of generative models , delivering competitive sample quality and likelihood scores on image and audio synthesis tasks ( SohlDickstein et al. , 2015 ; Song & Ermon , 2019 ; Ho et al. , 2020 ; Song et al. , 2021b ; Kingma et al. ... | The paper belongs to the class of diffusion-based generative models for generating synthetic images using class labels as guidance. It starts with a view of a recent work Dhariwal-Nichol (2021) where a classifier model is trained jointly with the diffusion-based generative model and the score (gradient of log probabili... | SP:5e647ea6f857d222ed538db72d17e7d806b2acde |
Triangle and Four Cycle Counting with Predictions in Graph Streams | 1 INTRODUCTION . Counting the number of cycles in a graph is a fundamental problem in the graph stream model ( e.g. , Atserias et al . ( 2008 ) ; Bera & Chakrabarti ( 2017 ) ; Seshadhri et al . ( 2013 ) ; Kolountzakis et al . ( 2010 ) ; Bar-Yossef et al . ( 2002 ) ; Kallaugher et al . ( 2019 ) ) . The special case of c... | The paper proposes a one pass streaming algorithms for estimating the number of triangles in adjacency list and arbitrary order models and 4-cycle in arbitrary edge arrival order. The authors propose algorithms for these streaming models under the assumption of a "heavy" edge oracle/ML model. The paper support theoreti... | SP:aa69cc79554eabf5cfd82a7f07ce851de9c16c7d |
Triangle and Four Cycle Counting with Predictions in Graph Streams | 1 INTRODUCTION . Counting the number of cycles in a graph is a fundamental problem in the graph stream model ( e.g. , Atserias et al . ( 2008 ) ; Bera & Chakrabarti ( 2017 ) ; Seshadhri et al . ( 2013 ) ; Kolountzakis et al . ( 2010 ) ; Bar-Yossef et al . ( 2002 ) ; Kallaugher et al . ( 2019 ) ) . The special case of c... | The line of research of improving sketching data structures/sampling with the help of learned models is becoming quite popular. This paper follows this line of research and applies this paradigm to the problem of cycle counting in graph streams ( specifically triangles and four-cycles). It provides a theoretical frame... | SP:aa69cc79554eabf5cfd82a7f07ce851de9c16c7d |
Triangle and Four Cycle Counting with Predictions in Graph Streams | 1 INTRODUCTION . Counting the number of cycles in a graph is a fundamental problem in the graph stream model ( e.g. , Atserias et al . ( 2008 ) ; Bera & Chakrabarti ( 2017 ) ; Seshadhri et al . ( 2013 ) ; Kolountzakis et al . ( 2010 ) ; Bar-Yossef et al . ( 2002 ) ; Kallaugher et al . ( 2019 ) ) . The special case of c... | Counting small length cycles in graph streams is an important graph mining primitive. For example, triangles, i.e., cycles of length 3, play an important role in analyzing social networks. Due to the importance of triangle counting, a wide variety of streaming algorithms in different graph steaming models have been pro... | SP:aa69cc79554eabf5cfd82a7f07ce851de9c16c7d |
Assessing two novel distance-based loss functions for few-shot image classification | 1 INTRODUCTION . Despite the advances in deep learning research , it remains a challenge for the standard supervised learning to achieve satisfactory results when learning from just a small amount of labeled data . Current deep learning algorithms tend to overfit when they are given a small dataset for training , reduc... | The paper proposes two losses for meta-learning-based few-shot learning. The first loss is a triplet loss where the positive and negative anchors are replaced by class prototypes (averages of class members from the train set of each episode). The second loss is ICNN proposed in Garcıa and Ramırez (2021) for a different... | SP:8994616fe3cd4886dbe9fbe8dc341daced8f3917 |
Assessing two novel distance-based loss functions for few-shot image classification | 1 INTRODUCTION . Despite the advances in deep learning research , it remains a challenge for the standard supervised learning to achieve satisfactory results when learning from just a small amount of labeled data . Current deep learning algorithms tend to overfit when they are given a small dataset for training , reduc... | This paper applies two losses to the few-shot learning model based on metric learning (similar to ProtoNet), which aims to utilize the intra- and inter-class distances. The first one is based on the original triplet loss and adjusted for the prototype network. The second one is based on the recently proposed Inter and ... | SP:8994616fe3cd4886dbe9fbe8dc341daced8f3917 |
Assessing two novel distance-based loss functions for few-shot image classification | 1 INTRODUCTION . Despite the advances in deep learning research , it remains a challenge for the standard supervised learning to achieve satisfactory results when learning from just a small amount of labeled data . Current deep learning algorithms tend to overfit when they are given a small dataset for training , reduc... | This paper discusses the few-shot learning based on metric learning. In order to better measure the distance between samples of different classes, the authors propose two new metric loss terms considering both inter-class and intra-class distances of samples. The first metric loss is called Proto-Triplet Loss, which im... | SP:8994616fe3cd4886dbe9fbe8dc341daced8f3917 |
Public Data-Assisted Mirror Descent for Private Model Training | In this paper , we revisit the problem of using public data to improve the privacy/utility trade-offs for differentially private ( DP ) model training . Here , public data refers to auxiliary data sets that have no privacy concerns . We consider public training data sets that are from the same distribution as the priva... | In this paper, the authors study differentially private empirical risk minimization (DP-ERM). Specifically, they study the case where the constraint set $\mathcal{C}$ has additional geometric structure, i.e., its Gaussian width could much lower than the underlying dimension $p$, such as the $\ell_1$-norm ball. The pape... | SP:8e0a5b11775310ef86e9d6a1631776ed8846794a |
Public Data-Assisted Mirror Descent for Private Model Training | In this paper , we revisit the problem of using public data to improve the privacy/utility trade-offs for differentially private ( DP ) model training . Here , public data refers to auxiliary data sets that have no privacy concerns . We consider public training data sets that are from the same distribution as the priva... | In this paper, a new algorithm has been proposed which leverages in-distribution public data to provide improvements in private training. The algorithm uses the loss on public data (with a strongly convex loss function) as a “mirror map” to implement private mirror descent on the private data. It is shown to give dimen... | SP:8e0a5b11775310ef86e9d6a1631776ed8846794a |
Public Data-Assisted Mirror Descent for Private Model Training | In this paper , we revisit the problem of using public data to improve the privacy/utility trade-offs for differentially private ( DP ) model training . Here , public data refers to auxiliary data sets that have no privacy concerns . We consider public training data sets that are from the same distribution as the priva... | The paper is in the continuation of recent line of work that studies private algorithms when it has access to some public data. They also achieve a dimension independent bound as in some of the previous work. The idea of the paper is very simple: they use public data as the mirror map in the private mirror descent algo... | SP:8e0a5b11775310ef86e9d6a1631776ed8846794a |
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation | 1 INTRODUCTION . Deep neural network have achieved remarkable success in a wide range of application scenarios but it still suffers poor generalization performance to other new domain because of the domain shift problem ( Csurka , 2017 ; Zhao et al. , 2020 ; Zhang et al. , 2020 ; Oza et al. , 2021 ) . To handle this is... | In this work, the authors propose a method for domain adaptation by introducing a new way of generating pseudo labels and a cross-transformer with classification and distillation losses. The authors used a cross-transformer where the queries come from the source domain and the values and keys come from the target domai... | SP:c7c6429978499249cee3d57596a0143bbde4bd7c |
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation | 1 INTRODUCTION . Deep neural network have achieved remarkable success in a wide range of application scenarios but it still suffers poor generalization performance to other new domain because of the domain shift problem ( Csurka , 2017 ; Zhao et al. , 2020 ; Zhang et al. , 2020 ; Oza et al. , 2021 ) . To handle this is... | This paper proposes a weight-sharing triple-branch transformer framework, or CDTrans for unsupervised domain adaptation. A two-way center-aware labeling method is proposed to provide better pseudo-labels. SOTA performances were achieved via the proposed method. | SP:c7c6429978499249cee3d57596a0143bbde4bd7c |
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation | 1 INTRODUCTION . Deep neural network have achieved remarkable success in a wide range of application scenarios but it still suffers poor generalization performance to other new domain because of the domain shift problem ( Csurka , 2017 ; Zhao et al. , 2020 ; Zhang et al. , 2020 ; Oza et al. , 2021 ) . To handle this is... | This submission proposes a transformer framework for unsupervised domain adaptive classification tasks. In this submission, they conduct an exploration about cross attention layer and found that the cross attention layer is robust to pseudo label noise. Inspired by this, they construct a three branches architecture in ... | SP:c7c6429978499249cee3d57596a0143bbde4bd7c |
Offline Reinforcement Learning for Large Scale Language Action Spaces | 1 INTRODUCTION . Building an end-to-end task-oriented dialogue agent is one of the promising applications of natural language processing ( NLP ) tasks , yet challenging due to large language action spaces and limited availability of human-annotated data . Recently , large-scale pre-trained language models ( LM ) have a... | This paper presents a reinforcement learning-based approach to building a task-oriented dialogue agent. Given a dialogue dataset annotated with rewards, the state-action value function is first trained by minimizing temporal differences. Then, a new training dataset is created by using the best actions selected among t... | SP:075222515d247ab5a2b691fa625741c5e2c9f2b9 |
Offline Reinforcement Learning for Large Scale Language Action Spaces | 1 INTRODUCTION . Building an end-to-end task-oriented dialogue agent is one of the promising applications of natural language processing ( NLP ) tasks , yet challenging due to large language action spaces and limited availability of human-annotated data . Recently , large-scale pre-trained language models ( LM ) have a... | This paper proposes an offline RL method applied to an end-to-end task-oriented dialogue model, where the proposed GPT-Critic is built on GPT-2 and fine-tuned on the self-generated sentences for policy updating. The paper claims that it is free from the issue of diverging from human language (a common issue in standar... | SP:075222515d247ab5a2b691fa625741c5e2c9f2b9 |
Offline Reinforcement Learning for Large Scale Language Action Spaces | 1 INTRODUCTION . Building an end-to-end task-oriented dialogue agent is one of the promising applications of natural language processing ( NLP ) tasks , yet challenging due to large language action spaces and limited availability of human-annotated data . Recently , large-scale pre-trained language models ( LM ) have a... | The paper works on offline reinforcement learning for natural language action space setting, particularly for task-oriented dialogue management. The paper nicely incorporate the policy network (to sample agent response) and the q network (to evaluate the agent response) into a single GPT-2 network and propose a policy ... | SP:075222515d247ab5a2b691fa625741c5e2c9f2b9 |
Relational Surrogate Loss Learning | 1 INTRODUCTION . Evaluation metrics matter in machine learning since it depicts how well we want the models to perform . Nevertheless , most of them are non-differentiable and non-decomposable , thus we can not directly optimize them during training but resort to loss functions ( or surrogate losses ) , which serve exa... | - This paper proposes a relational surrogate loss learning method (ReLoss) inspired by the fact that the evaluation metric and loss are used to distinguish whether one model is better or worse than another. - This paper provides extensive experiments that demonstrate the effectiveness of the proposed method. The perf... | SP:d901a65eae29085b39b42a527be4b01bb9370a30 |
Relational Surrogate Loss Learning | 1 INTRODUCTION . Evaluation metrics matter in machine learning since it depicts how well we want the models to perform . Nevertheless , most of them are non-differentiable and non-decomposable , thus we can not directly optimize them during training but resort to loss functions ( or surrogate losses ) , which serve exa... | The authors introduce a relational surrogate loss learning method (ReLoss) for replacing the original losses. The rationale and intuition behind are well-grounded. Experiments on various tasks and ablation studies prove the validity. | SP:d901a65eae29085b39b42a527be4b01bb9370a30 |
Relational Surrogate Loss Learning | 1 INTRODUCTION . Evaluation metrics matter in machine learning since it depicts how well we want the models to perform . Nevertheless , most of them are non-differentiable and non-decomposable , thus we can not directly optimize them during training but resort to loss functions ( or surrogate losses ) , which serve exa... | This paper proposes a surrogate loss learning method named ReLoss. The ReLoss learned by maximizing the relation between surrogate losses and evaluation metrics is used to replace the original losses. Extensive experiments on computer vision (CV) tasks (image classification, pose estimation, and scene text recognition)... | SP:d901a65eae29085b39b42a527be4b01bb9370a30 |
Label Leakage and Protection in Two-party Split Learning | 1 INTRODUCTION . With increasing concerns over data privacy in machine learning , federated learning ( FL ) ( McMahan et al. , 2017 ) has become a promising direction of study . Based on how sensitive data are distributed among parties , FL can be classified into different categories , notable among which are horizonta... | This paper formulates a threat model on two-party split learning (parties have different features, with one party holding the labels) for binary classification, and provides insights about how simple functions on the gradients can be used to extract confidential label information. The authors then proceed with defenses... | SP:1d660d8b2497c51b08143c85a1969ddd76da2bc4 |
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