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Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks | 1 INTRODUCTION The laws driving the physical world are often best described by partial differential equations ( PDEs ) that relate how a magnitude of interest changes in time with its change in space . They describe how the atmosphere and oceans circulate and interact , how structures deform under load and how electrom... | This paper proposes a new model for learning partial differential equations from data. The PDE is first discretized then solved as an ODE. The dynamics function is learned with Message-Passing Neural Networks, where the function is split into a sum of physically informed terms. This splitting both improves model perfor... | SP:a76c1a2b18015e647fa687abbb2840e2426b31f8 |
Towards fast and effective single-step adversarial training | 1 INTRODUCTION . Deep neural networks have achieved remarkable performance on a variety of tasks ( He et al. , 2015 ; Silver et al. , 2016 ; Devlin et al. , 2019 ) . However , it is well known that they are vulnerable to small worst-case perturbations around the input data – commonly referred to as adversarial examples... | This paper methodically studied the catastrophic overfitting in fast adversarial training (Fast-AT), and revisited the role of noise and clipping operation in Fast-AT. Based on the empirical findings, this paper discovered that the absence of clipping as well as using stronger noise could help avoid catastrophic overfi... | SP:9f8b2c2983b0825ed4867509162980586d12cde1 |
Towards fast and effective single-step adversarial training | 1 INTRODUCTION . Deep neural networks have achieved remarkable performance on a variety of tasks ( He et al. , 2015 ; Silver et al. , 2016 ; Devlin et al. , 2019 ) . However , it is well known that they are vulnerable to small worst-case perturbations around the input data – commonly referred to as adversarial examples... | This paper aims to address a failure mode in the traditional single-step adversarial training known as catastrophic overfitting. They show that compared to the common practice of generating the adversarial perturbation, adopting larger random initialization and avoiding clipping the perturbation can effectively mitigat... | SP:9f8b2c2983b0825ed4867509162980586d12cde1 |
Planckian jitter: enhancing the color quality of self-supervised visual representations | 1 INTRODUCTION . Self-supervised learning enables the learning of visual representation without the need for any labeled data ( Doersch et al. , 2015 ; Dosovitskiy et al. , 2014 ) . Several recent works learn representations that are invariant with respect to a set of data augmentations , and have obtained spectacular ... | **Overview.** This paper proposes a novel type of image colour augmentation to be used during self-supervised learning (SSL). **Background.** In a typical SSL setting, similar samples are generated by randomly augmenting an image in a variety of different ways: random cropping, colour jittering, random rotations, etc.... | SP:fa7fa24dcbfa67ffc00471e14aea2ed451bb1bea |
Planckian jitter: enhancing the color quality of self-supervised visual representations | 1 INTRODUCTION . Self-supervised learning enables the learning of visual representation without the need for any labeled data ( Doersch et al. , 2015 ; Dosovitskiy et al. , 2014 ) . Several recent works learn representations that are invariant with respect to a set of data augmentations , and have obtained spectacular ... | This paper first examines that typical color jittering augmentation is harmful to feature representation learning. Then the authors proposed a physics-based color augmentation, called Planckian jitter to improve the performance. The proposed Planckian jitter performs better with the recent contrastive and self-supervis... | SP:fa7fa24dcbfa67ffc00471e14aea2ed451bb1bea |
miniF2F: a cross-system benchmark for formal Olympiad-level mathematics | 1 INTRODUCTION . Shared benchmarks and datasets have historically played a crucial role in driving advances in largescale applications of deep learning , e.g . in computer vision ( Deng et al. , 2009 ) and natural language processing ( Wang et al. , 2019 ; Rajpurkar et al. , 2016 ; Paperno et al. , 2016 ) . Neural theo... | This paper presents miniF2F, a test suite of Olympiad-level problems of theorem proving that is implemented in Metamath, Lean and Isabelle. MiniF2F contains 488 individual theorem statements that are formalized from Olympiad math contests. GPT-f models trained on Metamath and Lean are evaluated on this test suite. | SP:b27e82ceb1636e24042a76b3749d729029ebb38c |
miniF2F: a cross-system benchmark for formal Olympiad-level mathematics | 1 INTRODUCTION . Shared benchmarks and datasets have historically played a crucial role in driving advances in largescale applications of deep learning , e.g . in computer vision ( Deng et al. , 2009 ) and natural language processing ( Wang et al. , 2019 ; Rajpurkar et al. , 2016 ; Paperno et al. , 2016 ) . Neural theo... | The authors present miniF2F, a dataset of formalized mathematical problems drawn from diverse sources including IMO, AIME, AMC, undergraduate, and high school problems. The focus is on algebra, inequalities, and number theory as those problems are easier to formalize than for example, geometry or combinatorial problems... | SP:b27e82ceb1636e24042a76b3749d729029ebb38c |
A New Perspective on Fluid Simulation: An Image-to-Image Translation Task via Neural Networks | 1 INTRODUCTION . 1.1 TARGET ISSUE . Simulating fluids , streams , and flows is a task in many fields of science . In most cases , this is done by numerical approaches like the method of finite elements ( FEM ) ( Quarteroni & Valli , 2008 ) or Lattice Boltzmann methods ( LBM ) ( Mohamad , 2011 ) . These methods are prov... | The paper casts the problem of 2d fluid flow simulation as an image to image-to-translation task. A cGAN with standard architectures (U-net generator, PatchGAN discriminator) is trained to advance the visualization of the simulation to the next time step, and an extension with an LSTM block is explored. The model is ev... | SP:fb34d6a5be0e6820eeac331137163919d581f120 |
A New Perspective on Fluid Simulation: An Image-to-Image Translation Task via Neural Networks | 1 INTRODUCTION . 1.1 TARGET ISSUE . Simulating fluids , streams , and flows is a task in many fields of science . In most cases , this is done by numerical approaches like the method of finite elements ( FEM ) ( Quarteroni & Valli , 2008 ) or Lattice Boltzmann methods ( LBM ) ( Mohamad , 2011 ) . These methods are prov... | The authors pose fluid simulation as an image-to-image translation task. From this perspective, approximating fluid flow using a cGAN can potentially improve speed (at the expense of accuracy) over FEM methods. This can be useful in situations where accuracy can be traded off for speed (e.g. video games). The authors s... | SP:fb34d6a5be0e6820eeac331137163919d581f120 |
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm | Recently , large-scale Contrastive Language-Image Pre-training ( CLIP ) ( Radford et al. , 2021 ) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks . However , CLIP is quite data-hungry and requires 400M image-text pairs for pre-trai... | The paper proposes DeCLIP to further utilize the data potential by adding three training objectives to CLIP pre-training: 1) inspired by SimSiam and BERT, self-supervised objectives are added to both image and text; 2) they generate different views for both images and text, and apply contrastive objectives; 3) they sam... | SP:81cd76230b5fb152f865202149938069ef659ae7 |
Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm | Recently , large-scale Contrastive Language-Image Pre-training ( CLIP ) ( Radford et al. , 2021 ) has attracted unprecedented attention for its impressive zero-shot recognition ability and excellent transferability to downstream tasks . However , CLIP is quite data-hungry and requires 400M image-text pairs for pre-trai... | In this paper, the authors mitigate the data-hungriness of the CLIP model. The authors propose three directions: single-modality self-supervision; multi-view multi-modality contrastive learning, and nearest-neighbor supervision. With the proposed three components, the authors can achieve better or comparable results wi... | SP:81cd76230b5fb152f865202149938069ef659ae7 |
Causally Focused Convolutional Networks Through Minimal Human Guidance | 1 INTRODUCTION . Convolutional Neural Networks ( CNNs ) are more popular than any other techniques in image classification . The ability to automatically extract required features is one key factor behind the phenomenal success of these models . Image classification being used in critical application areas such as medi... | This paper proposes a user-guided training for CNN to focus more on casual features which are based on human perspective and termed as casually focused convolutional neural network. Human guidance is used to make a rough binary segmentation mask of foreground objects/pixels. For an input image, mask and label are bo... | SP:f25f1a55b9c3945008f4c769ee1cc6414016da1a |
Causally Focused Convolutional Networks Through Minimal Human Guidance | 1 INTRODUCTION . Convolutional Neural Networks ( CNNs ) are more popular than any other techniques in image classification . The ability to automatically extract required features is one key factor behind the phenomenal success of these models . Image classification being used in critical application areas such as medi... | The paper proposes to alternate the loss of classification CNNs by adding additional loss term that focuses the model's attention on the object present in the image rather than on the background. To do so, the model is provided with an additional input (a binary mask) that is used to guide the learning of the model. Th... | SP:f25f1a55b9c3945008f4c769ee1cc6414016da1a |
Equal Experience in Recommender Systems | 1 INTRODUCTION . Recommender systems are everywhere , playing a crucial role to support decision making and to decide what we experience in our daily life . One recent challenge concerning fairness arises when the systems are built upon biased historical data . Biased data due to polarized preferences of particular gro... | The presented study argued that a fair recommendation should be independent of both user and item. Therefore, the study introduced a new fairness notion, i.e., equal experience, and further incorporated this fairness notion as a regularisation term in the matrix completion framework to construct a fair recommender syst... | SP:1d98df05bd885aff11b20cd016d822e970752dec |
Equal Experience in Recommender Systems | 1 INTRODUCTION . Recommender systems are everywhere , playing a crucial role to support decision making and to decide what we experience in our daily life . One recent challenge concerning fairness arises when the systems are built upon biased historical data . Biased data due to polarized preferences of particular gro... | The paper is concerned with fairness in recommendations. Specifically, there are groups of users and groups of items. Previous work has modelled fairness as the constraint of all user groups having the same accuracy or as the prediction probability being independent of the item group or the user group. In this work, th... | SP:1d98df05bd885aff11b20cd016d822e970752dec |
Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings | 1 INTRODUCTION . Training deep models with sufficient generalizability is of fundamental importance , which demands immense training data with fine-grained annotations ( Krizhevsky et al. , 2012 ; Brown et al. , 2020 ) . In many fields , however , data labeling requires domain-specific knowledge , such as medicine ( So... | The paper presents SCGM, a new technique for solving the Cross-Granularity Few-Shot learning (CGFS) problem. CGFS is defined as the problem of adapting a classification model trained on coarse (“superclass”) labels to perform well on fine-grained labels, which consist of multiple “subclass” labels within each supercla... | SP:6c3b9e6e95025f24bb371dfeb598f5ebc049bbc7 |
Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings | 1 INTRODUCTION . Training deep models with sufficient generalizability is of fundamental importance , which demands immense training data with fine-grained annotations ( Krizhevsky et al. , 2012 ; Brown et al. , 2020 ) . In many fields , however , data labeling requires domain-specific knowledge , such as medicine ( So... | The paper introduces a setup with the goal to adapt from a coarse pretrained model to unseen fine-grained labels. This problem is formulated as a superclass-subclass latent model and learned with maximum likelihood via expectation-maximization. The proposed approach, super-class conditional Gaussian mixture (SCGM) mode... | SP:6c3b9e6e95025f24bb371dfeb598f5ebc049bbc7 |
$p$-Laplacian Based Graph Neural Networks | 1 INTRODUCTION . In this paper , we explore the usage of graph neural networks ( GNNs ) for semi-supervised node classification on graphs , especially when the graphs admit strong heterophily or noisy edges . Semisupervised learning problems on graphs are ubiquitous in a lot of real-world scenarios , such as user class... | This paper proposes a p-Laplacian based GNN to handle heterophilic graphs and graphs with non-informative topologies. Both the above cases are assumed in most existing GNN architectures and hence this work breaks away from the norm. This work proposes a discrete p-Laplacian message passing scheme which is derived from ... | SP:36ec768c7b2c29b3ee400d43e4a7fa0dc35ca6cf |
$p$-Laplacian Based Graph Neural Networks | 1 INTRODUCTION . In this paper , we explore the usage of graph neural networks ( GNNs ) for semi-supervised node classification on graphs , especially when the graphs admit strong heterophily or noisy edges . Semisupervised learning problems on graphs are ubiquitous in a lot of real-world scenarios , such as user class... | The paper derives the p-Laplacian message passing formula under the p-Laplacian based regularization framework and further proposes p-GNN architecture. Authors further justify the relations of p-Laplacian message passing with low and high-pass filters and the upper bound of one layer risk of p-GNNs. Experiments show th... | SP:36ec768c7b2c29b3ee400d43e4a7fa0dc35ca6cf |
Learning Distributionally Robust Models at Scale via Composite Optimization | 1 INTRODUCTION . Conventional machine learning problem aims at learning a model based on the assumption that training data and test data come from same data distribution . However , this assumption may not hold in various practical learning problems where there is label shift ( Zhang et al. , 2020a ) , distribution shi... | The paper views different variants of DRO are simply instances of a finite-sum composite optimization, from which efficient optimization algorithms were proposed. The convergence analysis was established for strongly-convex and non-convex settings. The effectiveness of the proposed algorithm are well demonstrated in e... | SP:ec5a3d26769b738d3cc12b0bfdfe90fb51fb4b36 |
Learning Distributionally Robust Models at Scale via Composite Optimization | 1 INTRODUCTION . Conventional machine learning problem aims at learning a model based on the assumption that training data and test data come from same data distribution . However , this assumption may not hold in various practical learning problems where there is label shift ( Zhang et al. , 2020a ) , distribution shi... | This paper targets on solving distributionally robust optimization (DRO) that considering distribution shifts in the data. In this paper, they show that how different variants of DRO are simply instances of a finite-sum composite optimization for which they provide scalable methods by utilizing variance reduction al... | SP:ec5a3d26769b738d3cc12b0bfdfe90fb51fb4b36 |
IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data | 1 INTRODUCTION . Reinforcement learning ( RL ) solves many challenging problems including the game playing ( Mnih et al. , 2015 ) and the robot control ( Levine et al. , 2016 ) , which focus on the single-agent RL environment , modeled as the Markov decision process ( Sutton and Barto , 2011 ) . However , there exist m... | The submission proposes a cooperative MARL problem settings in which the observation-action-reward tuples generated during training are unavailable with some non-zero probability. The submission suggests addressing this problem setting by first imputing the missing training data and what they call a mask based update. ... | SP:fa0ebf93ac2ed036fcd7d1e81761fefb43a4961e |
IA-MARL: Imputation Assisted Multi-Agent Reinforcement Learning for Missing Training Data | 1 INTRODUCTION . Reinforcement learning ( RL ) solves many challenging problems including the game playing ( Mnih et al. , 2015 ) and the robot control ( Levine et al. , 2016 ) , which focus on the single-agent RL environment , modeled as the Markov decision process ( Sutton and Barto , 2011 ) . However , there exist m... | Authors present a novel and effective method leveraging a generative adversarial approach to a specific MARL problem with regard to missing training data. The approach presented treats the missing data as targets of imputation, loosely similar to that of inpainting problems in computer vision, where missing pixels are ... | SP:fa0ebf93ac2ed036fcd7d1e81761fefb43a4961e |
Assisted Learning for Organizations with Limited Imbalanced Data | 1 INTRODUCTION . Modern distributed learning frameworks such as federated learning ( Shokri & Shmatikov , 2015 ; Konecny et al. , 2016 ; McMahan et al. , 2017 ) aim to improve the learning performance for a large number of learners that have limited data and computation/communication resources . These learning framewor... | This paper investigates a novel learning scenario, where the learner has limited access to the global data distribution and can share learned model parameters with a so-called service provider through multiple (but limited) rounds of interactions. The motivation is interesting and seemingly useful for the scenarios des... | SP:40a3502f03e9bef04f4f8c088e6b7dcb768846da |
Assisted Learning for Organizations with Limited Imbalanced Data | 1 INTRODUCTION . Modern distributed learning frameworks such as federated learning ( Shokri & Shmatikov , 2015 ; Konecny et al. , 2016 ; McMahan et al. , 2017 ) aim to improve the learning performance for a large number of learners that have limited data and computation/communication resources . These learning framewor... | This paper studies a novel problem setup where a learner has unbalanced data, and a service provider has complementary or sufficient data, and the learner needs to improve accuracy in as few rounds of communication as possible, where the communication in each round is unbounded. An algorithm AssistSGD is proposed and s... | SP:40a3502f03e9bef04f4f8c088e6b7dcb768846da |
Surprise Minimizing Multi-Agent Learning with Energy-based Models | sites.google.com/view/surprise-web/ 1 INTRODUCTION . The rise of RL has led to an increasing interest in the study of multi-agent systems ( Lowe et al. , 2017 ; Vinyals et al. , 2019 ) , commonly known as Multi-Agent Reinforcement Learning ( MARL ) . In the case of partially observable settings , MARL enables the learn... | The authors present a method to regularise the learning of Q values within Decentralised partially observable markov decision processes, where the regulariser is one that minimises surprise in some way across the population of agents within the environment. This it is argued allows the agents to avoid situations in wh... | SP:ff2c79dd5ef9325a3f48750082a994b3ad9be172 |
Surprise Minimizing Multi-Agent Learning with Energy-based Models | sites.google.com/view/surprise-web/ 1 INTRODUCTION . The rise of RL has led to an increasing interest in the study of multi-agent systems ( Lowe et al. , 2017 ; Vinyals et al. , 2019 ) , commonly known as Multi-Agent Reinforcement Learning ( MARL ) . In the case of partially observable settings , MARL enables the learn... | This paper introduced a suprise term in optimizing the policies (action-value functions in Q-learning) for solving the non-stationary challenge due to the rapid changes from the environment for MARL scenarios. This work not only proposed the concept of suprising value in the context of MARL, but also gave an operator (... | SP:ff2c79dd5ef9325a3f48750082a994b3ad9be172 |
Multimeasurement Generative Models | 1 INTRODUCTION . Consider a collection of i.i.d . samples { xi } ni=1 , assumed to have been drawn from an unknown distribution with density pX in Rd . An important problem in probabilistic modeling is the task of drawing independent samples from pX , which has numerous potential applications . This problem is typicall... | This paper introduced an alternative sampling method, with an application on generative models, by convolving an unknown distribution $p_x$ with a factorial kernel called multi-measurement noise model (MNM). The resulting M-density $p_y$ is smoother (easier to sample from), and is permutation invariant. Two factorial k... | SP:82d842008ef479c32545afd952f9d7db15a0baf5 |
Multimeasurement Generative Models | 1 INTRODUCTION . Consider a collection of i.i.d . samples { xi } ni=1 , assumed to have been drawn from an unknown distribution with density pX in Rd . An important problem in probabilistic modeling is the task of drawing independent samples from pX , which has numerous potential applications . This problem is typicall... | Given $n$ independent samples $x_i$ in a space of dimension $d$ drawn from an unknown distribution $p(x)$, this paper is interested in drawing new samples independent of $x_i$ but coming from the same unknown distribution p(x). The classical approach consists of learning $p(x)$ from $x_i$ (i.e., approximated by $\til... | SP:82d842008ef479c32545afd952f9d7db15a0baf5 |
Robust Generalization of Quadratic Neural Networks via Function Identification | 1 INTRODUCTION . Recent work has demonstrated that neural networks are not robust to shifts in the underlying data , including both distribution shifts ( i.e. , where the data comes from a new distribution independent of the neural network parameters ) ( Hendrycks & Dietterich , 2019 ; Taori et al. , 2020 ) as well as ... | The author proposed the concept of function identification to address the limitation of parameter identification in the over-parameterized setting. The function identification states that the outputs of quadratic neural networks with the empirically minimized parameters and the true parameters have bounded difference w... | SP:24a82292d86bf8f75e14cf09ac7c2e3af812df6e |
Robust Generalization of Quadratic Neural Networks via Function Identification | 1 INTRODUCTION . Recent work has demonstrated that neural networks are not robust to shifts in the underlying data , including both distribution shifts ( i.e. , where the data comes from a new distribution independent of the neural network parameters ) ( Hendrycks & Dietterich , 2019 ; Taori et al. , 2020 ) as well as ... | The paper considers uncertainty estimation in overparameterized shallow neural networks with quadratic activation function. In particular, the paper assumes a non-linear regression model where labels are generated by an aforementioned neural net f* and noise is bounded. Then, uncertainty estimation is in giving a confi... | SP:24a82292d86bf8f75e14cf09ac7c2e3af812df6e |
On the One-sided Convergence of Adam-type Algorithms in Non-convex Non-concave Min-max Optimization | 1 INTRODUCTION . As one of the most popular optimizers in supervised deep learning tasks like natural language processing ( Chowdhury , 2003 ) as well as the main workhorse of generative adversarial network training ( Goodfellow et al. , 2014 ) , Adam-type methods are widely used because of their minimal need for learn... | This paper analyzes the performance of Adam-type algorithms (AMSGrad, to be specific) in nonconvex nonconcave minimax optimization. The authors propose that Adam-type algorithms can converge to a stationary point with the standard MVI assumption and an even weaker one-sided MVI assumption. The authors verify their clai... | SP:768d2d6dcf6baec2092cb4587df7fe3566e4a27d |
On the One-sided Convergence of Adam-type Algorithms in Non-convex Non-concave Min-max Optimization | 1 INTRODUCTION . As one of the most popular optimizers in supervised deep learning tasks like natural language processing ( Chowdhury , 2003 ) as well as the main workhorse of generative adversarial network training ( Goodfellow et al. , 2014 ) , Adam-type methods are widely used because of their minimal need for learn... | This manuscript developed several algorithms (e.g., AMSGrad-EG, AMSGrad-EG-DRD) for nonconvex-nonconcave min-max optimization. The convergence result of AMSGrad-EG-DRD is shown under the one-sided MVI condition. Polynomial-time complexity results are established. Some toy experiments are conducted for GAN on MNIST and ... | SP:768d2d6dcf6baec2092cb4587df7fe3566e4a27d |
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions | 1 INTRODUCTION In recent years , machine learning gained importance in computational quantum physics and chemistry to accelerate material discovery by approximating quantum mechanical ( QM ) calculations ( Huang & von Lilienfeld , 2021 ) . In particular , a lot of work has gone into building surrogate models to reprodu... | The paper develops a neural network based variational ansatz for modeling wave functions. Authors build their model on top of FermiNet architecture with a few modifications: they use a different feature embedding approach that is invariant with respect to basic spatial symmetries. In addition, authors use a GNN “hypern... | SP:6b55a41e6cde3b4e740941d48c237127c982da27 |
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions | 1 INTRODUCTION In recent years , machine learning gained importance in computational quantum physics and chemistry to accelerate material discovery by approximating quantum mechanical ( QM ) calculations ( Huang & von Lilienfeld , 2021 ) . In particular , a lot of work has gone into building surrogate models to reprodu... | The paper presents a new meta-learning method for solving the Schrodingers equation using neural networks. This method combines an existing neural wave function model called FermiNet together with a GNN (MetaGNN) to solve the Schrodingers equation for multiple geometries simultaneously. The MetaGNN takes the atomic gra... | SP:6b55a41e6cde3b4e740941d48c237127c982da27 |
Universal Approximation Under Constraints is Possible with Transformers | Keywords : Constrained Universal Approximation , Probabilistic Attention , Transformer Networks , Geometric Deep Learning , Measurable Maximum Theorem , Non-Affine Random Projections . 1 INTRODUCTION . In supervised learning , we select a parameterized model f̂ : Rn ! Rm by optimizing a real-valued loss function1L over... | The paper under review studies the universal approximation theory with constraints. For any convex or non-convex compact set, a universal approximation is proved through a probabilistic transformer with constraints. Furthermore, a chart-free universal approximation is established on the Riemannian manifold with geodes... | SP:b3a424fda4f96b24f753105c1c0ca8b04ebb15e2 |
Universal Approximation Under Constraints is Possible with Transformers | Keywords : Constrained Universal Approximation , Probabilistic Attention , Transformer Networks , Geometric Deep Learning , Measurable Maximum Theorem , Non-Affine Random Projections . 1 INTRODUCTION . In supervised learning , we select a parameterized model f̂ : Rn ! Rm by optimizing a real-valued loss function1L over... | This paper presents a family of constrained universal approximation results for probabilistic transformers. The authors provide significant theoretical contributions for both convex and non-convex constraint sets. In my opinion, this represents a significant advance in our understanding of universality in ML. | SP:b3a424fda4f96b24f753105c1c0ca8b04ebb15e2 |
Exploring General Intelligence of Program Analysis for Multiple Tasks | 1 INTRODUCTION . With the development of information technology , computer programs are used in an increasingly wide range of fields . As the number and variety of programs continue to expand , the task of analyzing programs becomes more significant and complex . Common program analysis tasks include program classifica... | This paper presents a graph neural network-based approach to solve two binary analysis tasks (program classification and binary similarity detection). The key idea of the paper is to merge different forms of representation of binary code (compiler IR, assembly code, etc.). | SP:58544efe4373310d82b14a2822a0c4e34e810c25 |
Exploring General Intelligence of Program Analysis for Multiple Tasks | 1 INTRODUCTION . With the development of information technology , computer programs are used in an increasingly wide range of fields . As the number and variety of programs continue to expand , the task of analyzing programs becomes more significant and complex . Common program analysis tasks include program classifica... | This paper proposes a program analysis model based on graph neural networks that performs analysis on the assembly code of a program and uses Control Flow Graph (CFG), Call Graph (CG), and Data Flow Graph (DFG) of the program as inputs. The goal is to design a generalized model that can solve both source-code level tas... | SP:58544efe4373310d82b14a2822a0c4e34e810c25 |
Improving greedy core-set configurations for active learning with uncertainty-scaled distances | 1 INTRODUCTION . Active learning aims to identify the most informative data to label and include in supervised training . Often , these algorithms focus on reducing model variance , representing distributional densities , maximizing expected model change , or minimizing expected generalization error ( Kirsch et al. , 2... | In this paper, the authors propose to improve the vanilla greedy Core-set active learning algorithm by (1) weighting the distance with uncertainty (measured by doubt, $1-\max_yP(y|x)$) and (2) use beam search instead of greedy search where the beams are selected by average uncertainty. They show with several toy exampl... | SP:bb83b81f007ebd41f444ed55636f0c3b3ca6b2c0 |
Improving greedy core-set configurations for active learning with uncertainty-scaled distances | 1 INTRODUCTION . Active learning aims to identify the most informative data to label and include in supervised training . Often , these algorithms focus on reducing model variance , representing distributional densities , maximizing expected model change , or minimizing expected generalization error ( Kirsch et al. , 2... | This paper attempts to improve upon the greedy core-set for active learning (Sener and Savarese) by employing distances scaled by uncertainty. The proposed method then leverages a beam search algorithm to identify the best core-set configuration among candidates with the lowest log-confidence to yield further improveme... | SP:bb83b81f007ebd41f444ed55636f0c3b3ca6b2c0 |
Denoising Diffusion Gamma Models | 1 INTRODUCTION . Deep generative neural networks have shown significant progress over the last years . The main architectures for generation are : ( i ) VAE ( Kingma & Welling , 2013 ) based , for example , NVAE ( Vahdat & Kautz , 2020 ) and VQ-VAE ( Razavi et al. , 2019 ) , ( ii ) GAN ( Goodfellow et al. , 2014 ) base... | ## Summary This paper explores the use of a non-Gaussian diffusion process for Diffusion Probabilistic Models. Unlike the original work by Ho et al., the authors replace the diffusion process with a Markov chain with transition kernel defined by a Gamma distribution. They show that the similar (and necessary) propert... | SP:8c94149941c39da4ae567d924ac7925fa314e145 |
Denoising Diffusion Gamma Models | 1 INTRODUCTION . Deep generative neural networks have shown significant progress over the last years . The main architectures for generation are : ( i ) VAE ( Kingma & Welling , 2013 ) based , for example , NVAE ( Vahdat & Kautz , 2020 ) and VQ-VAE ( Razavi et al. , 2019 ) , ( ii ) GAN ( Goodfellow et al. , 2014 ) base... | This paper formulates a denoising diffusion probabilistic model, but with Gamma distributed noise instead of Gaussian noise. The claim is that the Gamma noise model shares many of the same useful properties as the Gaussian model (eg a variational bound on data log likelihood, and repeated application of Gamma noise rem... | SP:8c94149941c39da4ae567d924ac7925fa314e145 |
How to measure deep uncertainty estimation performance and which models are naturally better at providing it | 1 INTRODUCTION . Deep neural networks ( DNNs ) show great performance in a wide variety of application domains including computer vision , natural language understanding and audio processing . Successful deployment of these models , however , is critically dependent on providing an effective uncertainty estimation of t... | The paper provides an empirical comparison of uncertainty estimates obtained from 484 deep neural networks (DNN), trained for image classification tasks on the ImageNet dataset. They compared uncertainty estimation performance of different architectures and training strategies (knowledge distillation) on many quantitat... | SP:f28b29a379b100a68f8bc3e01146f6a7b06aebf8 |
How to measure deep uncertainty estimation performance and which models are naturally better at providing it | 1 INTRODUCTION . Deep neural networks ( DNNs ) show great performance in a wide variety of application domains including computer vision , natural language understanding and audio processing . Successful deployment of these models , however , is critically dependent on providing an effective uncertainty estimation of t... | The paper presents an evaluation of different models in their capacity to reflect epistemic and aleatoric uncertainty and reviews different methods for uncertainty performance measurement. Models are classifiers trained on ImageNet. The number of models reported is 484. The analysis also considers the cases when the d... | SP:f28b29a379b100a68f8bc3e01146f6a7b06aebf8 |
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning | 1 INTRODUCTION . Synthesizing data under differential privacy ( DP ) ( Dwork ( 2006 ; 2011 ) ; Dwork & Roth ( 2014 ) ) enables us to share the synthetic data and generative model with rigorous privacy guarantees . Particularly , DP approaches of data synthesis involving the use of deep generative models have received a... | This paper studied an important topic in the field of data synthesis: how to train a private deep generative model without reusing the original data. In this paper, the authors proposed a new framework that uses deep generative models to synthesize data in different private ways. Unlike popular gradient cleaning method... | SP:1e92779dd25fcac41115f5771712163690fcc2eb |
PEARL: Data Synthesis via Private Embeddings and Adversarial Reconstruction Learning | 1 INTRODUCTION . Synthesizing data under differential privacy ( DP ) ( Dwork ( 2006 ; 2011 ) ; Dwork & Roth ( 2014 ) ) enables us to share the synthetic data and generative model with rigorous privacy guarantees . Particularly , DP approaches of data synthesis involving the use of deep generative models have received a... | In this paper, the authors propose a novel differentially private approach to generate both continuous as well as discrete valued synthetic data. The authors utilize a one shot approach to providing the privacy, by first generating privatized embedding of the sensitive dataset. The privatized embeddings are then iterat... | SP:1e92779dd25fcac41115f5771712163690fcc2eb |
Hinge Policy Optimization: Rethinking Policy Improvement and Reinterpreting PPO | 1 INTRODUCTION . Reinforcement learning ( RL ) has served as a powerful framework for achieving optimal sequential decision making by directly interacting with the environment and learning from the underlying random process . Policy optimization , as a fundamental design principle of RL algorithms , iteratively searche... | This paper reinterprets the theory of PPO-clip based on the hinge policy optimization. They prove the global convergence of PPO by introducing some assumptions. Besides, they generalize the algorithm to a new family of policy-based algorithms by regarding the policy as a generalized classifier. | SP:7102235a9333e4d73a5f90d5241ec37ca3fe9345 |
Hinge Policy Optimization: Rethinking Policy Improvement and Reinterpreting PPO | 1 INTRODUCTION . Reinforcement learning ( RL ) has served as a powerful framework for achieving optimal sequential decision making by directly interacting with the environment and learning from the underlying random process . Policy optimization , as a fundamental design principle of RL algorithms , iteratively searche... | The paper proposes hinge policy optimization, a new theoretical framework for interpreting policy gradient algorithms as classification problems to be solved with a hinge loss. In this perspective, the sign of the advantage function becomes the label, and the difference in action probabilities between policy after and ... | SP:7102235a9333e4d73a5f90d5241ec37ca3fe9345 |
Estimating Instance-dependent Label-noise Transition Matrix using DNNs | In label-noise learning , estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers . Traditionally , the transition from clean labels to noisy labels ( i.e. , clean label transition matrix ) has been widely exploited to learn a clean label cl... | The paper proposes to estimate an Instance-Dependent Noise (IDN) label transition matrix. Instead of modelling the clean label transition as typically done in previous literature, the authors propose to estimate the Bayes label transition using a DNN, motivate by several advantages including theoretically guaranteed Ba... | SP:54c86a69dc233f3a53e816f66089ab72a7997bac |
Estimating Instance-dependent Label-noise Transition Matrix using DNNs | In label-noise learning , estimating the transition matrix is a hot topic as the matrix plays an important role in building statistically consistent classifiers . Traditionally , the transition from clean labels to noisy labels ( i.e. , clean label transition matrix ) has been widely exploited to learn a clean label cl... | The transition matrix plays a vital role in modeling label noise. Current methods focus on modeling the transition from clean labels to noisy labels. While this paper alternatively models the transition from Bayes optimal labels to noisy labels. Since we usually use the Bayes optimal labels for prediction. This transfo... | SP:54c86a69dc233f3a53e816f66089ab72a7997bac |
FedMorph: Communication Efficient Federated Learning via Morphing Neural Network | 1 INTRODUCTION . Federated Learning ( FL ) ( Li et al. , 2021 ; Bonawitz et al. , 2019 ; Kairouz et al. , 2019 ; Li et al. , 2019 ) ensures data privacy by decoupling the training on the dataset in local clients and model aggregation in the global server . The iterative process of the local updates optimized on each cl... | The paper proposes FedMorph to address the communication and computation heterogeneity problem in federated learning. At every round, small morphed sub networks are send to clients for local training. The server updates the global model by distilling from the aggregated morphed subnets. | SP:47cd92b480c1b66cd5c559da3909188d43e0db87 |
FedMorph: Communication Efficient Federated Learning via Morphing Neural Network | 1 INTRODUCTION . Federated Learning ( FL ) ( Li et al. , 2021 ; Bonawitz et al. , 2019 ; Kairouz et al. , 2019 ; Li et al. , 2019 ) ensures data privacy by decoupling the training on the dataset in local clients and model aggregation in the global server . The iterative process of the local updates optimized on each cl... | The nature of FL workloads poses three evident system and machine learning challenges: communication overheads to broadcast models to clients and send locally-updated models back to the server; compute overheads, since clients might be in the form of constrained devices or battery powered; and overfitting, due to the a... | SP:47cd92b480c1b66cd5c559da3909188d43e0db87 |
Generalization to Out-of-Distribution transformations | 1 INTRODUCTION . Humans have a unique ability to generalize beyond the scope of prior experience ( Chollet , 2019 ; Lake et al. , 2017 ; Marcus , 2001 ) , while artificial agents struggle to apply knowledge to distributions outside the convex hull of their training data ( Santoro et al. , 2018 ; Lake & Baroni , 2018 ) ... | The authors trained autoencoders and variational autoencoders in cartesian space, as well as autoencoders in log-polar space, with generated data representing a range of interesting transformations. They then tested the ability of the models to extrapolate beyond the learned transformations in pixel space. The authors ... | SP:00f68b4f5ceeddc2e6b93cfdf1a75599bffd2acb |
Generalization to Out-of-Distribution transformations | 1 INTRODUCTION . Humans have a unique ability to generalize beyond the scope of prior experience ( Chollet , 2019 ; Lake et al. , 2017 ; Marcus , 2001 ) , while artificial agents struggle to apply knowledge to distributions outside the convex hull of their training data ( Santoro et al. , 2018 ; Lake & Baroni , 2018 ) ... | At its heart, the paper explores which inductive biases (and image representations) enable CNNs to generalize better across 2D transformations including rotations, translations and scale variations. The paper starts by proposing a methodology for generating a controlled dataset of binary masks consisting of random poly... | SP:00f68b4f5ceeddc2e6b93cfdf1a75599bffd2acb |
Conditional Expectation based Value Decomposition for Scalable On-Demand Ride Pooling | 1 INTRODUCTION . Taxi/car on Demand ( ToD ) services ( e.g. , UberX , Lyft , Grab ) not only provide a comfortable means of transport for customers , but also are good for the environment by enabling sharing of vehicles over time ( while being used to serve one request at any one point in time ) . A further improvement... | This paper considers the ridesharing matching problem and builds the solution upon NeurADP. The main contribution over NeurADP is that the action values of each agent (vehicle) takes into account the impact of its action on the neighboring agents within the same cluster, which is obtained through clustering of the inte... | SP:728f326478128c886426a8b9b103db36a47aa5a5 |
Conditional Expectation based Value Decomposition for Scalable On-Demand Ride Pooling | 1 INTRODUCTION . Taxi/car on Demand ( ToD ) services ( e.g. , UberX , Lyft , Grab ) not only provide a comfortable means of transport for customers , but also are good for the environment by enabling sharing of vehicles over time ( while being used to serve one request at any one point in time ) . A further improvement... | This paper studies an RPM problem (ride-pool matching problem) for on-demand transportation services. This problem is recently studied in various papers, but it is hard to choose a good matching by just using a bipartite graph matching due to the future demands, and it is an online decision-making problem. A recent br... | SP:728f326478128c886426a8b9b103db36a47aa5a5 |
Learning Dynamics Models for Model Predictive Agents | 1 INTRODUCTION . Recently reinforcement learning ( RL ) ( Sutton & Barto , 2018 ) , in particular actor-critic approaches ( Lillicrap et al. , 2015 ) were shown to successfully solve a variety of continuous control problems ( Schulman et al. , 2017 ; Lillicrap et al. , 2015 ; Fujimoto et al. , 2018 ; Haarnoja et al. , ... | This paper investigates different choices of designing the planning model which predict the next state on the observation space. The paper compares (1) prediction with deterministic or stochastic models, (2) using 1-step forward prediction loss or multi-step forward prediction loss to train the model, (3) prediction wi... | SP:0fdb3d5169a5c900e3bc0c496d656c5d3395c710 |
Learning Dynamics Models for Model Predictive Agents | 1 INTRODUCTION . Recently reinforcement learning ( RL ) ( Sutton & Barto , 2018 ) , in particular actor-critic approaches ( Lillicrap et al. , 2015 ) were shown to successfully solve a variety of continuous control problems ( Schulman et al. , 2017 ; Lillicrap et al. , 2015 ; Fujimoto et al. , 2018 ; Haarnoja et al. , ... | This work ablates some of the design choices that go into learning a dynamics model for control-based environments. They ablate 4 choices: use of deterministic vs. stochastic models, multistep losses, network ensembles, and input noise. The authors study this in a few of the DeepMind control suite environments. | SP:0fdb3d5169a5c900e3bc0c496d656c5d3395c710 |
Neural Combinatorial Optimization with Reinforcement Learning : Solving theVehicle Routing Problem with Time Windows | 1 INTRODUCTION . Vehicle routing problem with time windows ( VRPTW ) can be defined as an extension of the wellknown vehicle routing problem ( VRP ) in which the objective is to design a network of routes to satisfy customers demands with minimal total costsGan et al . ( 2012 ) . Each route starts from and ends at the ... | The paper proposed to solve a vehicle routing problem with time windows using neural network and reinforcement learning framework. An attention based encoder-decoder model is used to predict the distribution over problem instances while satisfying the problem constraints. Then a RL framework is trained to optimize the... | SP:2d246377df0afb9665f4ea9f2c510e9f48cc6d8c |
Neural Combinatorial Optimization with Reinforcement Learning : Solving theVehicle Routing Problem with Time Windows | 1 INTRODUCTION . Vehicle routing problem with time windows ( VRPTW ) can be defined as an extension of the wellknown vehicle routing problem ( VRP ) in which the objective is to design a network of routes to satisfy customers demands with minimal total costsGan et al . ( 2012 ) . Each route starts from and ends at the ... | This paper proposes a Neural Combinatorial Optimization approach to solving the Vehicle Routing Problem with Time Windows. It uses a policy gradient method to optimize an attention model, paired with a masking scheme that prevents unwanted actions during the policy rollout. Performance is compared with OR-Tools and LKH... | SP:2d246377df0afb9665f4ea9f2c510e9f48cc6d8c |
Dense Gaussian Processes for Few-Shot Segmentation | 1 INTRODUCTION . Image few-shot segmentation ( FSS ) of semantic classes ( Shaban et al. , 2017 ) has received increased attention in recent years . The aim is to segment novel query images based on only a handful annotated training samples , usually referred to as the support set . The FSS method thus needs to extract... | This paper proposes a special Gaussian process (GP) named dense GP, to model a mapping between dense local deep features and their corresponding mask values. Based on this dense GP, a few-shot segmentation method named DGPNet is proposed. The authors claim that DGPNet is novel in that it can be applied to situations th... | SP:951b5c2a6eba45d57baecfde6cbfbc732e1347ba |
Dense Gaussian Processes for Few-Shot Segmentation | 1 INTRODUCTION . Image few-shot segmentation ( FSS ) of semantic classes ( Shaban et al. , 2017 ) has received increased attention in recent years . The aim is to segment novel query images based on only a handful annotated training samples , usually referred to as the support set . The FSS method thus needs to extract... | Authors propose a novel few-shot segmentation method by adopting dense Gaussian process (GP) regression to capture complex appearance distributions. To boot the performance, authors consider the uncertainty in the final segmentation. Authors exploit the end-to-end learning capabilities of the proposed method to learn a... | SP:951b5c2a6eba45d57baecfde6cbfbc732e1347ba |
Only tails matter: Average-Case Universality and Robustness in the Convex Regime | 1 Introduction . The analysis of the average complexity of algorithms has a long story in computer science . Average-case complexity , for instance , drives much of the decisions made in cryptography ( Bogdanov & Trevisan , 2006 ) . Despite their relevance , average-case analyses are difficult to extend to other algori... | -- EDIT: I have updated my scores in response to clarifications -- The problem of optimizing a convex quadratic function via first order methods is considered. This is a well-understood problem from the worst case point of view, and its complexity will depend on the largest and smallest eigenvalues of the associated H... | SP:43329ddc4ce5ca94bde0ed3df97a040d090a4b41 |
Only tails matter: Average-Case Universality and Robustness in the Convex Regime | 1 Introduction . The analysis of the average complexity of algorithms has a long story in computer science . Average-case complexity , for instance , drives much of the decisions made in cryptography ( Bogdanov & Trevisan , 2006 ) . Despite their relevance , average-case analyses are difficult to extend to other algori... | The paper considers the problem of average convergence rate of first order methods on a given ensemble of quadratic problems. The authors propose the Generalized Chebyshev Method (GCM) and show that it is optimal when the e.s.d. is beta distribution. They also show that so long as we know the behavior of e.s.d. near th... | SP:43329ddc4ce5ca94bde0ed3df97a040d090a4b41 |
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them | 1 INTRODUCTION . Building models that are robust to adversarial examples ( Szegedy et al. , 2014 ; Biggio et al. , 2013 ) is a major challenge and open-problem in machine learning . Due to the inherent difficulty in building robust classifiers , researchers have attempted to build techniques to at least detect adversar... | This paper considers one important question: how to fairly compare the adversarial robustness between detection-based defenses and classification-based defenses. From the theoretical perspective, the authors show that: one can always (ideally) construct a robust classifier from a robust detector which has equivalent ro... | SP:e2212b3da261410c7b2714361d85d1b6876d4a3d |
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them | 1 INTRODUCTION . Building models that are robust to adversarial examples ( Szegedy et al. , 2014 ; Biggio et al. , 2013 ) is a major challenge and open-problem in machine learning . Due to the inherent difficulty in building robust classifiers , researchers have attempted to build techniques to at least detect adversar... | Adversarial examples are test time attacks in which the input is modified by up to distance \eps (under some metric) and the goal of adversarially robust learning is to have high (generalized) accuracy even under such attacks. One way to make predictions is to always output a label. Another way is to “abstain/detect” w... | SP:e2212b3da261410c7b2714361d85d1b6876d4a3d |
Gradient Matching for Domain Generalization | 1 INTRODUCTION The goal of domain generalization is to train models that performs well on unseen , out-of-distribution data , which is crucial in practice for model deployment in the wild . This seemingly difficult task is made possible by the presence of multiple distributions/domains at train time . As we have seen i... | The work tries to tackle the problem of domain generalisation in multi-source setting. The main claim of the paper is that by maximising inner product between gradients from different domains leads to better learning of domain invariant features. The provide a meta-learning inspired algorithm Fish to approximate the s... | SP:4e39dea3cdfdad801112d4894b73503345de78dc |
Gradient Matching for Domain Generalization | 1 INTRODUCTION The goal of domain generalization is to train models that performs well on unseen , out-of-distribution data , which is crucial in practice for model deployment in the wild . This seemingly difficult task is made possible by the presence of multiple distributions/domains at train time . As we have seen i... | This paper proposed inter-domain gradient matching for domain generalization. They also approximated the proposed model with a simple first-order algorithm to avoid costly second-order computations. The performance on the WILDs and DomainBed seems better than the ERM algorithm. | SP:4e39dea3cdfdad801112d4894b73503345de78dc |
Learning to Efficiently Sample from Diffusion Probabilistic Models | 1 INTRODUCTION . Denoising Diffusion Probabilistic Models ( DDPMs ) have emerged as a powerful class of generative models ( Sohl-Dickstein et al. , 2015 ; Ho et al. , 2020 ) . DDPMs model the data distribution through an iterative denoising process , and have been applied successfully to a variety of applications , inc... | This work presents a method to efficiently sample from a pre-trained DDPM by solving a dynamic programming problem that can maximize the log likelihood of the data samples given a fixed computational budget. This is done by defining a least-cost path problem to select a reduced set of time steps among a full grid of po... | SP:11ab8f635d8d593fe0187875679a36257360bf66 |
Learning to Efficiently Sample from Diffusion Probabilistic Models | 1 INTRODUCTION . Denoising Diffusion Probabilistic Models ( DDPMs ) have emerged as a powerful class of generative models ( Sohl-Dickstein et al. , 2015 ; Ho et al. , 2020 ) . DDPMs model the data distribution through an iterative denoising process , and have been applied successfully to a variety of applications , inc... | Samples are generated from DDPMs by solving an SDE (often in "discrete time", which is used to refer to specifically the Euler--Maruyama discretisation). This necessitates a choice for where to make numerical steps. Each choice of step locations has a corresponding ELBO. This paper demonstrates that (on a pretrained mo... | SP:11ab8f635d8d593fe0187875679a36257360bf66 |
Generalizing Successor Features to continuous domains for Multi-task Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) tackles sequential decision making problems by defining optimal behavior through a reward function , where the agent learns how to behave through interacting with the environment and receiving rewards . The ability of RL algorithms to generalize across different , yet rela... | This work looks at the use of successor features for solving simple continuous control tasks (in particular, reaching to different locations and door closing). The two contributions they enumerate are a ``practical implementation of SF framework for continuous state and action domains'' and jointly learning $\phi$ and ... | SP:bbb70a2512ecbd1fbef0f9219b1d3423a4b6ed83 |
Generalizing Successor Features to continuous domains for Multi-task Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) tackles sequential decision making problems by defining optimal behavior through a reward function , where the agent learns how to behave through interacting with the environment and receiving rewards . The ability of RL algorithms to generalize across different , yet rela... | The paper proposes a method to incorporate Successor Features (SFs) in domains with continuous state and action spaces. It proposes an actor-critic architecture (a variation of the Soft Actor-Critic method) that learns disentangled representations for the environment dynamics and the tasks. The network architecture gu... | SP:bbb70a2512ecbd1fbef0f9219b1d3423a4b6ed83 |
On The Quality Assurance Of Concept-Based Representations | 1 INTRODUCTION . Addressing the lack of interpretability of deep neural networks ( DNNs ) has given rise to explainability methods , most common of which are feature importance methods ( Ribeiro et al. , 2016 ; Lundberg & Lee , 2017 ) that quantify the contribution of input features to certain predictions ( Bhatt et al... | The authors have put decent effort to bring concept-based representation learning and disentanglement learning together under one umbrella in terms of the quality of generated concepts in presence as well as absence of ground truth concept labels. Some related metrics were proposed for evaluation of the quality of conc... | SP:586c729c2c163cba6c8a0519dd853463bbc405b7 |
On The Quality Assurance Of Concept-Based Representations | 1 INTRODUCTION . Addressing the lack of interpretability of deep neural networks ( DNNs ) has given rise to explainability methods , most common of which are feature importance methods ( Ribeiro et al. , 2016 ; Lundberg & Lee , 2017 ) that quantify the contribution of input features to certain predictions ( Bhatt et al... | The authors consider the question of whether recent concept-based learning algorithms, as well disentangled representation learning algorithms, result in high-quality representations. In particular, they consider what high-quality should mean in terms of the relationship with ground truth concepts and the ability to ma... | SP:586c729c2c163cba6c8a0519dd853463bbc405b7 |
Implicit Jacobian regularization weighted with impurity of probability output | Gradient descent ( GD ) plays a crucial role in the success of deep learning , but it is still not fully understood how GD finds minima that generalize well . In many studies , GD has been understood as a gradient flow in the limit of vanishing learning rate . However , this approach has a fundamental limitation in exp... | In this paper, the relationship between the Jacobian (the gradient of the final activation w.r.t parameters) and the Hessian is analyzed for the softmax cross-entropy loss. As a key tool, the approximation of the Hessian with the probability vector (the softmax output) is used, which suggests connections with several o... | SP:0134f562a484fc11e69847eb132d866e55fad86f |
Implicit Jacobian regularization weighted with impurity of probability output | Gradient descent ( GD ) plays a crucial role in the success of deep learning , but it is still not fully understood how GD finds minima that generalize well . In many studies , GD has been understood as a gradient flow in the limit of vanishing learning rate . However , this approach has a fundamental limitation in exp... | The authors study the largest eigenvalue and eigenvector of the Hessian of the loss function. The authors approximate the Hessian matrix by a low-dimensional matrix, which is a rank-one modification of a diagonal matrix. The eigendecomposition helps to explain how the sharpness influences the gradient descent method an... | SP:0134f562a484fc11e69847eb132d866e55fad86f |
White Paper Assistance: A Step Forward Beyond the Shortcut Learning | 1 INTRODUCTION . We don ’ t see things as they are ; we see them as we are . –An Old Proverb These words give us insight into the predictable irrationalities of the human mind . Individuals always create their own “ subjective reality ” from their perception . Psychological researches ( Haselton et al. , 2015 ; Zhang e... | The paper proposes a novel method, White Paper Assistant (WP), to prevent CNNs from utilizing spurious input-out correlations, the so-called shortcuts, in classification. The main idea is to intermittently update the CNN to predict uniform distribution over classes for white image inputs. Through careful and extensive ... | SP:3a268e208ecbf0a5dfa031a6bf54314f5df558c9 |
White Paper Assistance: A Step Forward Beyond the Shortcut Learning | 1 INTRODUCTION . We don ’ t see things as they are ; we see them as we are . –An Old Proverb These words give us insight into the predictable irrationalities of the human mind . Individuals always create their own “ subjective reality ” from their perception . Psychological researches ( Haselton et al. , 2015 ; Zhang e... | The present work introduces an approach to tackle Shortcut Learning by CNNs, called White Paper Assistance (WP). After motivating and introducing the method, the authors evaluate it on computer vision datasets with synthetic inserted shortcuts, ie. black pixels in the corners of the images. The authors show that the WP... | SP:3a268e208ecbf0a5dfa031a6bf54314f5df558c9 |
Exact Stochastic Newton Method for Deep Learning: the feedforward networks case. | The inclusion of second-order information into Deep Learning optimization has drawn consistent interest as a way forward to improve upon gradient descent methods . Estimating the second-order update is computationally expensive , which drastically limits its usage scope and forces the use of various truncations and app... | This paper claims that it is possible to compute the Newton method's update exactly for deep neural networks (multi-layer perceptrons). The motivation is that Newton's method, as a second-order optimizer that includes loss curvature information, should improve upon first-order optimizers, such as gradient descent, that... | SP:5820c97c44d7ad06c16cf5e7ce4f8b197ea08c94 |
Exact Stochastic Newton Method for Deep Learning: the feedforward networks case. | The inclusion of second-order information into Deep Learning optimization has drawn consistent interest as a way forward to improve upon gradient descent methods . Estimating the second-order update is computationally expensive , which drastically limits its usage scope and forces the use of various truncations and app... | This paper proposes a stochastic second order method to train neural network under some specific regularisation criterion. The method is based on the Sifrian, an extension of the Lagrangian that splits the definition of the gradient of each layer's parameter as different constraints with their own multiplier. Solving t... | SP:5820c97c44d7ad06c16cf5e7ce4f8b197ea08c94 |
If your data distribution shifts, use self-learning | 1 INTRODUCTION . Deep Neural Networks ( DNNs ) can reach human-level performance in complex cognitive tasks ( Brown et al. , 2020 ; He et al. , 2016a ; Berner et al. , 2019 ) if the distribution of the test data is sufficiently similar to the training data . However , DNNs are known to struggle if the distribution of t... | The paper studies effectiveness of self-training to improve test time performance when the distribution of test data is not similar to the training data. The paper more specifically focuses on source-free domain adaptation settings where the source target data is not available. In this setup self-training has been test... | SP:810e4d1edb1d7aa02ef0777f45ce4db3263d551c |
If your data distribution shifts, use self-learning | 1 INTRODUCTION . Deep Neural Networks ( DNNs ) can reach human-level performance in complex cognitive tasks ( Brown et al. , 2020 ; He et al. , 2016a ; Berner et al. , 2019 ) if the distribution of the test data is sufficiently similar to the training data . However , DNNs are known to struggle if the distribution of t... | This paper provides an in depth empirical evaluation of classical self-training techniques such as pseudo-labelling and entropy minimization on test performance under domain shifts. The authors stress that, although simple, these techniques consistently improve the robustness to distribution shifts regardless of model ... | SP:810e4d1edb1d7aa02ef0777f45ce4db3263d551c |
Theoretical Analysis of Consistency Regularization with Limited Augmented Data | 1 INTRODUCTION . Modern machine learning models , especially deep learning models , require abundant training samples . Since data collection and human annotation are expensive , data augmentation has been a ubiquitous practice in creating artificial labeled samples and improving generalization performance . This pract... | Data augmentation is a common technique to improve generalization, especially when data is scarce. This paper introduces a theoretical framework for analyzing the effectiveness of consistency regularization when data augmentation is employed. In the limit, consistency regularization is akin to solving a constrained opt... | SP:54e2b82691851b880425f85be0279b423132edfb |
Theoretical Analysis of Consistency Regularization with Limited Augmented Data | 1 INTRODUCTION . Modern machine learning models , especially deep learning models , require abundant training samples . Since data collection and human annotation are expensive , data augmentation has been a ubiquitous practice in creating artificial labeled samples and improving generalization performance . This pract... | This paper aims to offer a theoretical analysis of the training with data augmentation and associated consistency loss. While it is intuitive that training with data augmentation and consistency loss will help, this paper offers a theoretical justification of the intuitions. The simple framework (to view DAC as a hypot... | SP:54e2b82691851b880425f85be0279b423132edfb |
Relative Molecule Self-Attention Transformer | 1 INTRODUCTION . Predicting molecular properties is central to applications such as drug discovery or material design . Without accurate prediction of properties such as toxicity , a promising drug candidate is likely to fail clinical trials . Many molecular properties can not be feasibly computed ( simulated ) from fi... | The paper proposes a new transformer network architecture to pre-train the molecule datasets. Based on the Molecule Attention Transformer, the proposed model, R-MAT, incorporates a few handcrafted features into the self-attention layer of a transformer architecture. The features can incorporate distances between atoms ... | SP:9f3699227642cf22c764b80be6bb9917fc7bce8a |
Relative Molecule Self-Attention Transformer | 1 INTRODUCTION . Predicting molecular properties is central to applications such as drug discovery or material design . Without accurate prediction of properties such as toxicity , a promising drug candidate is likely to fail clinical trials . Many molecular properties can not be feasibly computed ( simulated ) from fi... | This paper proposes a relative self-attention layer for the Transformer model. The relative self-attention of two atoms consists of their relative distance, their shortest path distance in the molecular graph, and their physiochemical. The proposed relative molecule attention Transformer can be first pretrained with a ... | SP:9f3699227642cf22c764b80be6bb9917fc7bce8a |
Hyperspherical embedding for novel class classification | Deep neural networks proved to be useful to learn representations and perform classification on many different modalities of data . Traditional approaches work well on the closed set problem . For learning tasks involving novel classes , known as the open set problem , the metric learning approach has been proposed . H... | This paper is about classification of images in an open set setting. Data coming from new classes are introduced to the network after training on data from a fixed set of known classes. The goal is to be able to correctly classify the old and new classes either jointly or not. This paper proposes a method for handling ... | SP:609072c4e2753277ab90174dc3a7b66d03653498 |
Hyperspherical embedding for novel class classification | Deep neural networks proved to be useful to learn representations and perform classification on many different modalities of data . Traditional approaches work well on the closed set problem . For learning tasks involving novel classes , known as the open set problem , the metric learning approach has been proposed . H... | The paper proposes an approach to Few-shot Learning based on the the CosFace (Normalized Softmax) Loss. After pretraining, class weights are added to the cross entropy loss for each new class in the test set, which are computed by averaging over inferred weights from a support set while fixing the remaining network wei... | SP:609072c4e2753277ab90174dc3a7b66d03653498 |
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) ( Sutton & Barto , 1998 ) has achieved significant successes in many fields ( Mnih et al. , 2015 ; Silver et al. , 2017 ; OpenAI , 2019 ; Afsar et al. , 2021 ) , robotics ( Deisenroth et al. , 2013 ) , playing Go ( Silver et al. , 2016 ; 2017 ) , Starcraft ( Vinyals et al.... | The paper studies the problem of learning under constrained Markov decision processes. It proposes to solve this problem by an RL algorithm based on generalized advantage estimator (GAE), which they call Conservative Update Policy (CUP). Although existing works have already proposed GAE-based algorithms, the current p... | SP:fe24152df3ec630eed6ec8adbf88d3a044e78123 |
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) ( Sutton & Barto , 1998 ) has achieved significant successes in many fields ( Mnih et al. , 2015 ; Silver et al. , 2017 ; OpenAI , 2019 ; Afsar et al. , 2021 ) , robotics ( Deisenroth et al. , 2013 ) , playing Go ( Silver et al. , 2016 ; 2017 ) , Starcraft ( Vinyals et al.... | This paper considers reinforcement learning, a common model which has seen success recently in many areas (e.g. games, robotics, autonomous vehicles). Prototypical reinforcement learning algorithms explore the action space in order to maximize their rewards as much as possible, potentially ignoring impacts of the chos... | SP:fe24152df3ec630eed6ec8adbf88d3a044e78123 |
On Adversarial Bias and the Robustness of Fair Machine Learning | 1 INTRODUCTION . Trustworthiness is a crucial requirement of machine learning algorithms in critical decision making processes , as highlighted by many AI regulations and policies as well as technical research papers . Algorithmic fairness is at the core of trust requirements for automated decision making in sensitive ... | Intuitively, the same amount of data poisoning will have a larger impact when the learner is solving a constrained optimization problem than an unconstrained one. This paper proposed an attack algorithm specifically designed for such fair learners and showed that fair learning algorithms are more vulnerable to adversa... | SP:9861ba00add665c624c186f948063da0cdff0cff |
On Adversarial Bias and the Robustness of Fair Machine Learning | 1 INTRODUCTION . Trustworthiness is a crucial requirement of machine learning algorithms in critical decision making processes , as highlighted by many AI regulations and policies as well as technical research papers . Algorithmic fairness is at the core of trust requirements for automated decision making in sensitive ... | This paper studies the problem of fair classification when the dataset is adversarially perturbed. In particular, the authors considers two models of adversarial perturbation -- (1) adversarial sampling (outlier data points are chosen adversarially), and (2) adversarial labeling (labels of a fraction of data points ar... | SP:9861ba00add665c624c186f948063da0cdff0cff |
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models | 1 INTRODUCTION . Implicit deep learning models such as Neural ODEs ( Chen et al. , 2018 ) , OptNets ( Amos and Zico Kolter , 2017 ) or Deep Equilibrium models ( DEQs ) ( Bai et al. , 2019 ; 2020 ) have recently emerged as a way to train deep models with infinite effective depth without the associated memory cost . Inde... | In implicit deep learning such as deep equilibrium models, computing the inverse Jacobian for the forward pass is computationally expensive. This paper propose an interesting approach to combine the information from the forward and backward pass to make an efficient estimate of the Jacobian inverse. In one approach, th... | SP:24819c1c943d8b83dd7e048a5bb4cf66fc5cc43e |
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models | 1 INTRODUCTION . Implicit deep learning models such as Neural ODEs ( Chen et al. , 2018 ) , OptNets ( Amos and Zico Kolter , 2017 ) or Deep Equilibrium models ( DEQs ) ( Bai et al. , 2019 ; 2020 ) have recently emerged as a way to train deep models with infinite effective depth without the associated memory cost . Inde... | In various machine learning problems which can be formulated as a bilevel optimization problem and solved using gradient-based methods, the computation of hypergradients is necessary. However, the involved inverse Jacobian matrix in the hypergradient has been a computational bottleneck in high-dimensional settings. Thi... | SP:24819c1c943d8b83dd7e048a5bb4cf66fc5cc43e |
Gating Mechanisms Underlying Sequence-to-Sequence Working Memory | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) transform stimuli across multiple time-points to produce nonlinear working memory representations that can be used to solve complex tasks ( Elman , 1990 ; Hochreiter & Schmidhuber , 1997 ; Mante et al. , 2013 ) . Memorization and manipulation of discrete sequences of ... | This paper attempts to understand how a RNN goes about solving the Variable Copy Delay Memory task in fine detail. Their model has been trained perfectly on this task and so they are able to focus on how it goes about changing its cell values in the case. The authors present different metrics to track resetting and mem... | SP:df975cc1c367d216509f31196a7eff5ad95e570a |
Gating Mechanisms Underlying Sequence-to-Sequence Working Memory | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) transform stimuli across multiple time-points to produce nonlinear working memory representations that can be used to solve complex tasks ( Elman , 1990 ; Hochreiter & Schmidhuber , 1997 ; Mante et al. , 2013 ) . Memorization and manipulation of discrete sequences of ... | The paper analyzes GRU's underlying mechanisms that store and retrieve information in the delay copy task of a sequence of K symbols. It proposes a perturbation-based method to determine which neurons are responsible for encoding a step-element pair. The paper then shows that at each step of the decoding phase, certain... | SP:df975cc1c367d216509f31196a7eff5ad95e570a |
Adversarial twin neural networks: maximizing physics recovery for physical system | 1 INTRODUCTION . Internet of Everything ( IoE ) expands quickly to interconnect various devices . The systematic planning , modeling , and control of IoE can bring many benefits to society ( Li et al. , 2020 ) . However , it remains an open question on how to efficiently model various grids with different levels of sys... | The paper proposes ATN to model and identify physical systems. Here, the physical systems are grids consisting of measurement sensors: because the sensor configuration is not perfect, there can be unobservable parts in the grid system. The authors assume that there are physics bases that can represent the output with a... | SP:82f5968efa5604cda6804cbd87b1ebb6582d1b4e |
Adversarial twin neural networks: maximizing physics recovery for physical system | 1 INTRODUCTION . Internet of Everything ( IoE ) expands quickly to interconnect various devices . The systematic planning , modeling , and control of IoE can bring many benefits to society ( Li et al. , 2020 ) . However , it remains an open question on how to efficiently model various grids with different levels of sys... | The task of identifying a physical system on a graph is addressed. While the main part of the to-be-estimated model is assumed to be linear, the proposed model needs a nonlinear part (which is modeled by a neural net) due to the presence of unobserved nodes. The authors use a combination of a sparse linear model and a ... | SP:82f5968efa5604cda6804cbd87b1ebb6582d1b4e |
A Generalized Weighted Optimization Method for Computational Learning and Inversion | 1 INTRODUCTION . Given N data pairs { xj , yj } Nj=1 , where xj ∈ R , yj ∈ C , j = 1 , . . . , N , we are interested in learning a random Fourier feature ( RFF ) model ( Rahimi & Recht , 2008 ; Liao et al. , 2020 ; Xie et al. , 2020 ) fθ ( x ) = P−1∑ k=0 θke ikx , x ∈ [ 0 , 2π ] , ( 1 ) where P ∈ N is a given positive ... | This paper studies the weighted least squares, random features model under noise one-dimensional data setting in under-/over-parameterized regime. The derived error bounds demonstrate the impact of noise on the generalization error. Besides, the extension to kernel regression shows that, the selected weighted matrix is... | SP:35355126f30b88391404bdea921a944e9e9da117 |
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