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Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting | 1 INTRODUCTION . Traffic forecasting is a challenging problem due to complex road networks , varying patterns in the data , and intertwined dependencies among models . This implies that prediction methods should not only find intrinsic spatio-temporal dependencies among many roads , but also quickly respond to irregula... | This paper explores a new direction of model design in traffic forecasting tasks. It proposes a neural memory module to model the spatio-temporal traffic data and designs a new traffic forecasting model based on the memory module. Experiments on a few public datasets demonstrate the effectiveness of the proposed scheme... | SP:4a83a8ba8190703c509ecc17fbdc70e82e67d6c8 |
Deep Classifiers with Label Noise Modeling and Distance Awareness | 1 INTRODUCTION . While deep learning has led to impressive advances in predictive accuracy , models often still suffer from overconfidence and ill-calibrated uncertainties ( Ovadia et al. , 2019 ) . This is particularly problematic in safety-critical applications ( e.g. , healthcare , autonomous driving ) , where uncer... | This paper studies the combination between model uncertainty and data uncertainty based on spectral-normalized Gaussian process. Theoretical results show that heteroscedastic SNGP allows for joint modeling of model and data uncertainties. The paper then proposes an approximate inference scheme for efficient model train... | SP:e3ec7c4484ffce62c71a364bd6fadcad84dd5a3a |
Deep Classifiers with Label Noise Modeling and Distance Awareness | 1 INTRODUCTION . While deep learning has led to impressive advances in predictive accuracy , models often still suffer from overconfidence and ill-calibrated uncertainties ( Ovadia et al. , 2019 ) . This is particularly problematic in safety-critical applications ( e.g. , healthcare , autonomous driving ) , where uncer... | This paper presents a new method for model and data uncertainty estimation in deep neural networks combining the heteroscedastic method (Collier et al. 2020) and the Spectral-Normalised Gaussian Process (SNGP) method (Liu et al. 2020). It is shown that the two methods are complementary and their combination outperforms... | SP:e3ec7c4484ffce62c71a364bd6fadcad84dd5a3a |
Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos | 1 INTRODUCTION . Performative prediction ( Perdomo et al. , 2020 ) is a recently introduced framework that focuses on a natural but largely unexplored element of supervised learning . In many practical cases the predictive model can affect the very outcome that it is trying to predict . For example , predictions about ... | This paper studied the different behaviours of using exponentiated gradient descent (Def. 2.3) in linear regression with different learning rates. The setting is called performative prediction, which can be viewed as a special case of reinforcement learning (after the model makes a prediction, the environment returns a... | SP:fdab12cf54b6c3cff52990607369188060910f5c |
Multi-agent Performative Prediction: From Global Stability and Optimality to Chaos | 1 INTRODUCTION . Performative prediction ( Perdomo et al. , 2020 ) is a recently introduced framework that focuses on a natural but largely unexplored element of supervised learning . In many practical cases the predictive model can affect the very outcome that it is trying to predict . For example , predictions about ... | The paper analyzes the performative prediction setting (Perdomo 2020) where multiple agents perform gradient descent to converge to a performatively optimal point. The agent are modeled by constrained linear predictive models which are used for linear regression. The authors show that the learning dynamics converges ... | SP:fdab12cf54b6c3cff52990607369188060910f5c |
Causal discovery from conditionally stationary time-series | Causal discovery , i.e. , inferring underlying cause-effect relationships from observations of a scene or system , is an inherent mechanism in human cognition , but has been shown to be highly challenging to automate . The majority of approaches in the literature aiming for this task consider constrained scenarios with... | This paper proposes a new method for discovering the causal graph from time-series data when the time-series are generated by a non-stationary process. The method relies on previous work from Lowe et al, 2020 and proposes to condition the causal summary graph driving the (causal) edge generation between variables by a ... | SP:1c1dfcfd223ed681844ad09249052a51a6c1fcb0 |
Causal discovery from conditionally stationary time-series | Causal discovery , i.e. , inferring underlying cause-effect relationships from observations of a scene or system , is an inherent mechanism in human cognition , but has been shown to be highly challenging to automate . The majority of approaches in the literature aiming for this task consider constrained scenarios with... | This paper aims to solve the problem of causal summary graph extraction and time series reconstruction at the same time. They propose a conditional VAE based model. The model is conditioned on state variables $s$, which makes it different from a normal VAE. Experiments on two datasets show the method outperforms ACD on... | SP:1c1dfcfd223ed681844ad09249052a51a6c1fcb0 |
FastSHAP: Real-Time Shapley Value Estimation | 1 INTRODUCTION . With the proliferation of black-box models , Shapley values ( Shapley , 1953 ) have emerged as a popular explanation approach due to their strong theoretical properties ( Lipovetsky and Conklin , 2001 ; Štrumbelj and Kononenko , 2014 ; Datta et al. , 2016 ; Lundberg and Lee , 2017 ) . In practice , how... | This paper proposes FastSHAP to efficiently estimate the Shapley value in a single forward pass using a learned explainer model. Since there is no label to train the Shapley value estimator, stochastic gradient optimization using a weighted least squares-like objective function is applied to train FastSHAP. The experim... | SP:bffc59409af0acecdcb344728b3f18d404e9d6ea |
FastSHAP: Real-Time Shapley Value Estimation | 1 INTRODUCTION . With the proliferation of black-box models , Shapley values ( Shapley , 1953 ) have emerged as a popular explanation approach due to their strong theoretical properties ( Lipovetsky and Conklin , 2001 ; Štrumbelj and Kononenko , 2014 ; Datta et al. , 2016 ; Lundberg and Lee , 2017 ) . In practice , how... | The paper works on improving the runtime for estimating Shapley values. The work introduces FastSHAP that estimates Shapley values with a learned explainer model. The method is validated on tabular and image datasets (CIFAR10 and Imagenette). | SP:bffc59409af0acecdcb344728b3f18d404e9d6ea |
Topological Graph Neural Networks | 1 INTRODUCTION . Graphs are a natural description of structured data sets in many domains , including bioinformatics , image processing , and social network analysis . Numerous methods address the two dominant graph learning tasks of graph classification or node classification . In particular , graph neural networks ( ... | The authors present a topology analysis improvement to GCN, using persistent homology, to capture global information regarding the topology of the graph. The authors conduct several experiments, from graph to node classification, and also introduce two novel data sets to exemplify the importance of topology. In most ... | SP:6f83e65ba5408b86d451f5545bf49100e9771f30 |
Topological Graph Neural Networks | 1 INTRODUCTION . Graphs are a natural description of structured data sets in many domains , including bioinformatics , image processing , and social network analysis . Numerous methods address the two dominant graph learning tasks of graph classification or node classification . In particular , graph neural networks ( ... | This paper introduces TOGL, a new layer for Graph Neural Networks (GNN), making the GNN "aware" of topological information during this training phase. It differs from the closely related work *Graph Filtration Learning (GFL)* (although taking inspiration from it) as GFL is mostly a readout function (roughly, final laye... | SP:6f83e65ba5408b86d451f5545bf49100e9771f30 |
On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning | 1 INTRODUCTION . Perhaps the most famous algorithm in tabular reinforcement learning is the so-called Q-learning algorithm . Under very general conditions , it is well known that the Q-learning converges to the optimal Q-function with probability one ( Tsitsiklis , 1994 ; Jaakkola et al. , 1994 ) . Importantly , in ord... | This paper studies Monte Carlo with exploration starts algorithm for solving the reinforcement learning problem. The writing is clear and I enjoyed reading this paper. As for the results, asymptotic convergence of the algorithm is established without needing strong assumptions in related literature. As pointed out by t... | SP:0b81356b614ae533e975718f34af62efcf7a7bb9 |
On the Convergence of the Monte Carlo Exploring Starts Algorithm for Reinforcement Learning | 1 INTRODUCTION . Perhaps the most famous algorithm in tabular reinforcement learning is the so-called Q-learning algorithm . Under very general conditions , it is well known that the Q-learning converges to the optimal Q-function with probability one ( Tsitsiklis , 1994 ; Jaakkola et al. , 1994 ) . Importantly , in ord... | The paper studies the convergence of Monte Carlo Exploring Starts (MCES), in which the Q-function is estimated by averaging Monte Carlo returns and the policy is defined as the greedy policy w.r.t. this Q-function. The authors provide a technically simple proof of the convergence under different assumptions on the unde... | SP:0b81356b614ae533e975718f34af62efcf7a7bb9 |
Zero-Shot Recommender Systems | Performance of recommender systems ( RecSys ) relies heavily on the amount of training data available . This poses a chicken-and-egg problem for early-stage products , whose amount of data , in turn , relies on the performance of their RecSys . In this paper , we explore the possibility of zero-shot learning in RecSys ... | This paper studies "zero shot recommendation" where source and target domain have no overlap in terms of user and items. The paper proposes to use item content features, such as leveraging BERT on descriptions, instead of IDs. Experiments are conducted on two offline datasets. | SP:044eb7985596e0b826f3628855c2fb2913907cc7 |
Zero-Shot Recommender Systems | Performance of recommender systems ( RecSys ) relies heavily on the amount of training data available . This poses a chicken-and-egg problem for early-stage products , whose amount of data , in turn , relies on the performance of their RecSys . In this paper , we explore the possibility of zero-shot learning in RecSys ... | A great recommender system relies on great training set. However, at the beginning, there is no such data availability. This paper tries to solve the zero-shot recommendation problem where there is no user or item overlaps. The two challenges are generalize to unseen users and to unseen items. For unseens users, sequen... | SP:044eb7985596e0b826f3628855c2fb2913907cc7 |
AA-PINN: ATTENTION AUGMENTED PHYSICS INFORMED NEURAL NETWORKS | 1 INTRODUCTION . Computational Fluid Dynamics ( CFD ) has become the core technology behind almost every fluid simulation . Fluid mechanics has been traditionally concerned with big data , thus making deep learning an obvious choice in modelling the inherent complexity of the problem . Neural Networks of late has been ... | This manuscript introduces the attention module into the framework of physics-informed neural networks. The contributions are: (1) proposing a network architecture that marries the popular attention mechanism to physics-informed neural networks. (2) separating the large attention module by operating on the channel and ... | SP:4821cbd324cb4e3eb2a62c6d39d6c30d184d88b6 |
AA-PINN: ATTENTION AUGMENTED PHYSICS INFORMED NEURAL NETWORKS | 1 INTRODUCTION . Computational Fluid Dynamics ( CFD ) has become the core technology behind almost every fluid simulation . Fluid mechanics has been traditionally concerned with big data , thus making deep learning an obvious choice in modelling the inherent complexity of the problem . Neural Networks of late has been ... | The authors propose a new architecture and loss function for training physics-informed neural networks (PINNs) on fluid flow problems. The idea is centered around augmenting a basic residual network with two additional attention blocks that are placed before and after residual blocks. These two new blocks aim to introd... | SP:4821cbd324cb4e3eb2a62c6d39d6c30d184d88b6 |
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability | 1 INTRODUCTION . With the widespread deployment of deep neural networks in high-stakes applications such as medical imaging ( Kang et al. , 2017 ) , credit score assessment ( West , 2000 ) , and facial recognition ( Deng et al. , 2019 ) , practitioners need to understand why their models make the decisions they do . In... | This paper introduces a parameter-space saliency map to explore the salient parameters that are responsible for miscalssification. A set of experiments and visulizations are conducted on the salient parameters, leading to several interesting findings, such as, the nearest parameter neighbors share similar semantic info... | SP:ee9c1549531b0ed933020491aee532835368a862 |
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability | 1 INTRODUCTION . With the widespread deployment of deep neural networks in high-stakes applications such as medical imaging ( Kang et al. , 2017 ) , credit score assessment ( West , 2000 ) , and facial recognition ( Deng et al. , 2019 ) , practitioners need to understand why their models make the decisions they do . In... | This paper devises an analytic method for explainability based on the observation of filter-wise parameter saliency distribution, and tests on several models. And several experiments are conducted to deminstrate the conjecture. The motivation is straightforward and easy to understand. | SP:ee9c1549531b0ed933020491aee532835368a862 |
Linear Convergence of SGD on Overparametrized Shallow Neural Networks | 1 INTRODUCTION . Our understanding of the optimization landscape of supervised learning with neural networks has vastly improved in recent years . This is in part due to the observation that overparameterization is key to overcome the pitfalls of first-order methods in general non-convex problems ( Soltanolkotabi et al... | 1. This paper proves that SGD converges to a global minimum in certain non-convex problems assuming the loss function satisfies a growth condition. The proof relies on assuming that the initial Jacobian matrix is non-singular and shows that it stays non-singular since SGD iterates remain close to the initialization. 2... | SP:83f5e4ad9fa7ca35ada3a63eb113b1b1827b2926 |
Linear Convergence of SGD on Overparametrized Shallow Neural Networks | 1 INTRODUCTION . Our understanding of the optimization landscape of supervised learning with neural networks has vastly improved in recent years . This is in part due to the observation that overparameterization is key to overcome the pitfalls of first-order methods in general non-convex problems ( Soltanolkotabi et al... | This paper proved that a two-layer neural network with smooth activation and proper initialization can converge linearly to a global minima of training loss using mini-batch SGD when the width is larger than $\Omega(m^2/\sqrt{b})$ where $m$ is the number of training data and $b$ is batch size. As the batch size increas... | SP:83f5e4ad9fa7ca35ada3a63eb113b1b1827b2926 |
HD-cos Networks: Efficient Neural Architechtures for Secure Multi-Party Computation | 1 INTRODUCTION . Machine learning models are often trained with user data that may contain private information . For example , in healthcare patients diagnostics contain sensitive information and in financial sectors , user data contains potentially private information such as salaries and taxes . In these applications... | The paper proposes using a cosine activation function and Hadamard-Diagonal transformation as a means to improve the efficiency of MPC for machine learning. The paper considers a two-server model, where the training computation are carried out by two non-colluding parties. Experiments are provided to demonstrate the im... | SP:3a342130759e47057da1d63ad04c5e28e3955dbd |
HD-cos Networks: Efficient Neural Architechtures for Secure Multi-Party Computation | 1 INTRODUCTION . Machine learning models are often trained with user data that may contain private information . For example , in healthcare patients diagnostics contain sensitive information and in financial sectors , user data contains potentially private information such as salaries and taxes . In these applications... | MPC is a cryptographic technique to allow multiple party to jointly compute a protocol without leaking sensitive data, but building blocks in the neural network converted to MPC setup usually suffer from heavy communication overhead among parties, and jointly training ML models is also computationally expensive. So, in... | SP:3a342130759e47057da1d63ad04c5e28e3955dbd |
Contextual Multi-Armed Bandit with Communication Constraints | 1 INTRODUCTION . In the last few years , synergies between Machine Learning ( ML ) and communication networks have attracted a lot of interest in the research community , thanks to the fruitful interplay of the two fields in emerging applications from Internet of things ( IoT ) to autonomous vehicles and other edge ser... | This paper studies a CMAB problem where the actions for multiple agents are sent from the decision-maker over a rate-limited communication channel. The authors developed information-theoretic performance bound for Thompson sampling based policies, which reduce the problem to transmitting conditional probability distrib... | SP:68a322bf79a790fb6fac85ebb81f37ba3014d24b |
Contextual Multi-Armed Bandit with Communication Constraints | 1 INTRODUCTION . In the last few years , synergies between Machine Learning ( ML ) and communication networks have attracted a lot of interest in the research community , thanks to the fruitful interplay of the two fields in emerging applications from Internet of things ( IoT ) to autonomous vehicles and other edge ser... | This work studies a rate-constrained contextual multi arm bandit (RC-CMAB) problem: the decision maker has to make action decisions for multiple parallel (independent and identical) CMAB problems (i.e. agents), but can only communicate the actions for each CMAB problem to a controller through a rate-constrained communi... | SP:68a322bf79a790fb6fac85ebb81f37ba3014d24b |
Space-Time Graph Neural Networks | We introduce space-time graph neural network ( ST-GNN ) , a novel GNN architecture , tailored to jointly process the underlying space-time topology of time-varying network data . The cornerstone of our proposed architecture is the composition of time and graph convolutional filters followed by pointwise nonlinear activ... | This paper proposes a new deep learning architecture called ST-GNN that learns representations on graphs that evolve over time. Their work focuses on developing an interesting time-varying convolutional architecture, which exploits the graph-time underlying structure of the signals, processing across both the graph and... | SP:913e4b2754a85df298f868cd55e0f40b3e4e88a4 |
Space-Time Graph Neural Networks | We introduce space-time graph neural network ( ST-GNN ) , a novel GNN architecture , tailored to jointly process the underlying space-time topology of time-varying network data . The cornerstone of our proposed architecture is the composition of time and graph convolutional filters followed by pointwise nonlinear activ... | This paper introduces a new spatio-temporal Graph Neural Network, ST-GNN, for making predictions on temporal network. Its proposed space-time convolution operator is a composition of temporal convolution and graph diffusion. The paper further proves that under practical conditions their ST-GNN with Integral Lipschitz f... | SP:913e4b2754a85df298f868cd55e0f40b3e4e88a4 |
Deep Active Learning by Leveraging Training Dynamics | 1 INTRODUCTION . Training deep learning ( DL ) models usually requires a large amount of high-quality labeled data ( Zhang et al. , 2017 ) to optimize a model with a massive number of parameters . The acquisition of such annotated data is usually time-consuming and expensive , making it unaffordable in the fields that ... | The authors address the problem of active learning in the context of deep learning. Instead of querying new examples based on the decision boundary (which in nonlinear models can be tricky or even ill-defined), as it is usually done for linear models, they rely on the train-faster--generalize-better paradigm. Thus, the... | SP:f352e3f7154d22c662b5a80eb2a309ce0345b453 |
Deep Active Learning by Leveraging Training Dynamics | 1 INTRODUCTION . Training deep learning ( DL ) models usually requires a large amount of high-quality labeled data ( Zhang et al. , 2017 ) to optimize a model with a massive number of parameters . The acquisition of such annotated data is usually time-consuming and expensive , making it unaffordable in the fields that ... | This work proposes DynamicsAL, a novel AL criteria that selects new training example base on its ability to maximize the training dynamics $\frac{\partial}{\partial t} l(f, y)$. The authors supplied a practical algorithm (Section 3.2) and compared the proposed criteria with existing methods (Section 3.3). The author... | SP:f352e3f7154d22c662b5a80eb2a309ce0345b453 |
Vector-quantized Image Modeling with Improved VQGAN | Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot , few-shot , transfer learning and multi-tasking capabilities on both generative and discriminative language tasks . Motivated by this success , we explore a Vector-quantized Image Modeling ( VIM ) approach... | This paper mainly investigates how to further improve the image generation quality of previous work VQGAN, where several modifications are proposed to train a better quantized auto-encoder model. After modeling the discrete tokens with an auto-regressive transformer, we could observe that the generation results of the ... | SP:4b745d0f1a688e009c1eff19df47effd48a053ee |
Vector-quantized Image Modeling with Improved VQGAN | Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot , few-shot , transfer learning and multi-tasking capabilities on both generative and discriminative language tasks . Motivated by this success , we explore a Vector-quantized Image Modeling ( VIM ) approach... | This paper proposes vision transformer based VQGAN whose encoder and decoder are implemented as transformers rather than standard CNNs. It provides few improvements that demonstrates quantitatively better results in multiple datasets in unconditional / conditional image generation. It also shows how the proposed model ... | SP:4b745d0f1a688e009c1eff19df47effd48a053ee |
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP | 1 INTRODUCTION . With the advent of large corpora of unlabeled data in vision and language , self-supervised learning via contrastive learning has become highly successful . Some contrastive learning objectives , such as those of BYOL ( Grill et al. , 2020 ) and SimSiam ( Chen & He , 2021 ) , do not require negative sa... | By using the InfoNCE loss for model training, CLIP has achieved great success. In this paper, the authors propose CLOOB, short for "Contrastive Leave One Out Boost", where modern Hopfield networks are used together with the InfoLOOB objective. InfoLOOB is a leave-one-out upper bound of mutual information, and modern Ho... | SP:23873ae412ac3528036025fc3f53896c220984b4 |
CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP | 1 INTRODUCTION . With the advent of large corpora of unlabeled data in vision and language , self-supervised learning via contrastive learning has become highly successful . Some contrastive learning objectives , such as those of BYOL ( Grill et al. , 2020 ) and SimSiam ( Chen & He , 2021 ) , do not require negative sa... | This paper proposed a new contrastive learning method called CLOOB, which minimized the leave-one-out upper bound (InfoLOOB) on mutual information with the modern Hopfield networks. Concretely, Hopfield networks replace the original embeddings by retrieved embeddings in the InfoLOOB objective. The retrieved embeddings... | SP:23873ae412ac3528036025fc3f53896c220984b4 |
3D Pre-training improves GNNs for Molecular Property Prediction | Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts . Including 3D molecular structure as input to learned models improves their performance for many molecular tasks . However , this information is infeasible to compute at the scale required by seve... | This paper proposes a 3D pretraining method for molecular property prediction. As 3D information is infeasible to compute at the scale required by real-world applications, this paper reasons about the geometry of molecules given only their 2D molecular graphs. During pretraining with molecules whose 3D information is... | SP:9a904b1fc758f8875bf1f6c2c483ebfadbebb9e8 |
3D Pre-training improves GNNs for Molecular Property Prediction | Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts . Including 3D molecular structure as input to learned models improves their performance for many molecular tasks . However , this information is infeasible to compute at the scale required by seve... | The authors present 3D Infomax, a graph neural network (GNN) pre-training solution that leverages 3D information to generate better learned embeddings and improve performance on down-stream prediction tasks where 3D information would be useful but not easily obtainable. The approach is useful for a range of downstream... | SP:9a904b1fc758f8875bf1f6c2c483ebfadbebb9e8 |
Hierarchically Regularized Deep Forecasting | 1 INTRODUCTION . Multivariate time series forecasting is a key problem in many domains such as retail demand forecasting ( Böse et al. , 2017 ) , financial predictions ( Zhou et al. , 2020 ) , power grid optimization ( Hyndman & Fan , 2009 ) , road traffic modeling ( Li et al. , 2017 ) , and online ads optimization ( ... | The paper considers point forecasting of hierarchical time series, i.e. multivariate time series with hierarchical aggregation constraints. The authors propose a new approach based on decomposing the series along a global set of basis time series where (approximate) hierarchical constraints are applied on the coefficie... | SP:436ded90847e688e4edd7555ddf94838b4337dd4 |
Hierarchically Regularized Deep Forecasting | 1 INTRODUCTION . Multivariate time series forecasting is a key problem in many domains such as retail demand forecasting ( Böse et al. , 2017 ) , financial predictions ( Zhou et al. , 2020 ) , power grid optimization ( Hyndman & Fan , 2009 ) , road traffic modeling ( Li et al. , 2017 ) , and online ads optimization ( ... | The paper introduces a method for hierarchical time series forecasting. The problem setting is: given historical hierarchical univariate time series data and given historical and future features (like holidays, etc..), try to predict future values for all time series, while keeping the coherence constraints of the h... | SP:436ded90847e688e4edd7555ddf94838b4337dd4 |
DiBB: Distributing Black-Box Optimization | 1 INTRODUCTION . Black Box Optimization ( BBO ) can be applied , by definition , to any problem independent of the specific application ( Audet & Hare , 2017 ) . In principle , this provides a method that is applicable to problems yet unsolved by the current state of the art . The most obvious catch lies in their compu... | This paper suggests Distributing Black-Box Optimization (DiBB) framework that enables the running of black-box optimization techniques in a distributed manner. Under the assumption that some optimization variables are correlated in a negligible manner to the optimization objective values, the authors partition the vari... | SP:b92c785318f5e3774a4ef5c933c72015cbf80327 |
DiBB: Distributing Black-Box Optimization | 1 INTRODUCTION . Black Box Optimization ( BBO ) can be applied , by definition , to any problem independent of the specific application ( Audet & Hare , 2017 ) . In principle , this provides a method that is applicable to problems yet unsolved by the current state of the art . The most obvious catch lies in their compu... | This paper proposes Distributed Black Box optimization (DiBB), which involves using disjoint distributed pipelines to perform CMA/Hessian-based updates, over functions with assumed separability in terms of parameters. The method is explained in detail, and experiments are performed on BBOB functions (with varying dimen... | SP:b92c785318f5e3774a4ef5c933c72015cbf80327 |
Neural Parameter Allocation Search | 1 INTRODUCTION . Training neural networks requires ever more computational resources , with GPU memory often being a significant limitation ( Rajbhandari et al. , 2021 ) . Methods such as checkpointing ( e.g. , Chen et al. , 2016 ; Gomez et al. , 2017 ; Jain et al. , 2020 ) and out-of-core algorithms ( e.g. , Ren et al... | The paper presents a method to automatically select parameters to share between layers. It proposes to use a shape shifter network to either increase or decrease the number of parameters in the model. The parameters are mapped into parameter groups through a preliminary training step and k-mean cluster the layers. Laye... | SP:1a3b5863b4113fd70eb6e9d252b555f58c7414b9 |
Neural Parameter Allocation Search | 1 INTRODUCTION . Training neural networks requires ever more computational resources , with GPU memory often being a significant limitation ( Rajbhandari et al. , 2021 ) . Methods such as checkpointing ( e.g. , Chen et al. , 2016 ; Gomez et al. , 2017 ; Jain et al. , 2020 ) and out-of-core algorithms ( e.g. , Ren et al... | Parameter sharing can reduce memory footprint of neural networks and memory bandwidth requirements, but existing methods require manually tuning the sharing strategy. This paper uses a small phase of training to cluster the learned layer representations by groups. This allows networks to be scaled from small to large p... | SP:1a3b5863b4113fd70eb6e9d252b555f58c7414b9 |
Tuformer: Data-Driven Design of Expressive Transformer by Tucker Tensor Representation | Transformers are neural network architectures that achieve remarkable performance in many areas . However , the core component of Transformers , multi-head selfattention ( MHSA ) , is mainly derived from heuristics , and the interactions across its components are not well understood . To address the problem , we first ... | The paper investigates the multi-head self-attention mechanism (MHSA) of transformer networks through the lens of tensor decompositions via tensor diagram notation. The authors propose an extension to MHSA inspired by the Tucker decomposition (termed THSA), analyze its expressive power, and demonstrate that it belongs ... | SP:32abdf60d25c01e6a2025568fda331da4881e463 |
Tuformer: Data-Driven Design of Expressive Transformer by Tucker Tensor Representation | Transformers are neural network architectures that achieve remarkable performance in many areas . However , the core component of Transformers , multi-head selfattention ( MHSA ) , is mainly derived from heuristics , and the interactions across its components are not well understood . To address the problem , we first ... | Focusing on the multi-head self-attention (MHSA) structure, this paper proposes an extension of the tensor diagram to denote self-attention (SA) structures more intuitively. Then inspired by the Tucker format, this paper also proposes a new form of SA named Tucker-Head Self-Attention (THSA), which can also be illustrat... | SP:32abdf60d25c01e6a2025568fda331da4881e463 |
Bolstering Stochastic Gradient Descent with Model Building | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) is a popular optimization algorithm for machine learning problems which can achieve very fast convergence rates when its stepsize and its scheduling are tuned well for the specific application at hand . This tuning procedure can take up to thousands of CPU/GPU days r... | This paper proposes an alternative to stochastic line search which is based on forwarding step model building which corrects the direction of move and its magnitude at the same time. In its proposed algorithm it first checks if the given step size satisfies the stochastic line search. If yes then just use the step siz... | SP:8f8b8725508f06303fd8cb959f8fcb00455f4ba7 |
Bolstering Stochastic Gradient Descent with Model Building | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) is a popular optimization algorithm for machine learning problems which can achieve very fast convergence rates when its stepsize and its scheduling are tuned well for the specific application at hand . This tuning procedure can take up to thousands of CPU/GPU days r... | The authors proposed a method called stochastic model building (SMB) that uses a combination of existing techniques to get faster convergence in stochastic non-convex optimization. In particular, they use a stochastic adaptation of the model-building globalization strategy from Oztoprak and Birbil (2018), in which the ... | SP:8f8b8725508f06303fd8cb959f8fcb00455f4ba7 |
Neural graphical modelling in continuous-time: consistency guarantees and algorithms | 1 INTRODUCTION . This paper deals with learning directed graphs from a combination of temporal data and assumptions on the parameterization of the underlying structural dynamical system . Graphical models can offer a parsimonious , interpretable representation of the dynamics of stochastic processes , and have proven t... | This paper introduces a brand new graphical modeling framework from the perspective of neural ODEs. Traditionally structure learning involves using sampled data to learn the structure of graphs. This paper, however, looks at the graph structure learning problem from a different viewpoint, using continuous-time dynamics... | SP:c993d028cb6c4168ee0a40f62cb9020008ba8bf2 |
Neural graphical modelling in continuous-time: consistency guarantees and algorithms | 1 INTRODUCTION . This paper deals with learning directed graphs from a combination of temporal data and assumptions on the parameterization of the underlying structural dynamical system . Graphical models can offer a parsimonious , interpretable representation of the dynamics of stochastic processes , and have proven t... | The paper proposes to learn Jacobian-sparse neural network ODEs from irregular trajectories of a dynamical system. The main contribution is the sparsity of the ODE Jacobian, which results in learning of differential covariate causalities. Learning the differential structure is an important real-world problem. The propo... | SP:c993d028cb6c4168ee0a40f62cb9020008ba8bf2 |
Optimal Transport for Causal Discovery | 1 INTRODUCTION . Determining causal direction between two variables is a fundamental and challenging causal discovery task ( Janzing et al. , 2012 ) . Conventional constraint-based and score-based causal discovery methods identify causal structures only up to Markov equivalent classes ( Spirtes et al. , 2001 ) , in whi... | The paper tackles the problem of causal discovery in the basic case where a pair of variables is considered. In particular, it is concerned with Functional Causal Models and how to establlish the causal direction for a pair of continuous variables. It motivates the contibution by poiting out thet the performance of ava... | SP:10c1db18ba9a4ee43ab04f5d2fab030c6e8aecb3 |
Optimal Transport for Causal Discovery | 1 INTRODUCTION . Determining causal direction between two variables is a fundamental and challenging causal discovery task ( Janzing et al. , 2012 ) . Conventional constraint-based and score-based causal discovery methods identify causal structures only up to Markov equivalent classes ( Spirtes et al. , 2001 ) , in whi... | The authors frame the bivariate causal discovery problem in terms of the analysis of a dynamical-system. They use results from the field of optimal transport to interpret additive noise models from this framework. They also develop a novel criterion and a causal discovery algorithm based thereupon, and compare their re... | SP:10c1db18ba9a4ee43ab04f5d2fab030c6e8aecb3 |
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios | 1 INTRODUCTION . System identification , i.e. , learning models of dynamical systems from observed data ( Ljung ( 1998 ) ; Gevers ( 2005 ) ) , is a key ingredient of model-predictive control ( Camacho & Alba ( 2013 ) ) and model-based reinforcement learning ( RL ) . State space models ( Hamilton ( 1994 ) ; Jordan ( 200... | The paper proposes a method to learn a probabilistic recurrent state-space model for time-varying dynamics. The proposed method combines the Kalman filtering-based update rule with deep network-based encoder and decoder model. Effectively, the method can be used to replace RNN cells in a recurrent model, and is shown t... | SP:67a5ff36ee4df5da8185f078c826943f5c101898 |
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios | 1 INTRODUCTION . System identification , i.e. , learning models of dynamical systems from observed data ( Ljung ( 1998 ) ; Gevers ( 2005 ) ) , is a key ingredient of model-predictive control ( Camacho & Alba ( 2013 ) ) and model-based reinforcement learning ( RL ) . State space models ( Hamilton ( 1994 ) ; Jordan ( 200... | This work proposes to extend latent state-space models (SSMs) with a latent variable that changes the dynamics. Update equations akin to Kalman filtering are provided, along with a training loss and method. Experiments on several robotics tasks appear to indicate that the method performs well relative to alternative me... | SP:67a5ff36ee4df5da8185f078c826943f5c101898 |
Self-Organized Polynomial-time Coordination Graphs | 1 INTRODUCTION . Cooperative multi-agent reinforcement learning ( MARL ) is a promising approach to a variety of real-world applications , such as sensor networks ( Zhang & Lesser , 2011 ; Ye et al. , 2015 ) , traffic light control ( Van der Pol & Oliehoek , 2016 ) , and multi-robot formation ( Alonso-Mora et al. , 201... | This paper proposes an extension of deep coordination graph, called Self-Organized Polynomial-time Coordination Graphs (SOP-CG). Instead of pre-specified graph topology used in DCG, their method allows graph topology to be state-dependent, which is achieved by a coordinator agent, and the optimization of this agent is ... | SP:7a0df8dec9ab3e72c6daea7ed2288d1c59b6d2ec |
Self-Organized Polynomial-time Coordination Graphs | 1 INTRODUCTION . Cooperative multi-agent reinforcement learning ( MARL ) is a promising approach to a variety of real-world applications , such as sensor networks ( Zhang & Lesser , 2011 ; Ye et al. , 2015 ) , traffic light control ( Van der Pol & Oliehoek , 2016 ) , and multi-robot formation ( Alonso-Mora et al. , 201... | This paper introduced a novel method called Self-Organized Polynomial-time Coordination Graphs (SOP-CG), aiming to handle the decentralized constraint optimization problem (DCOP). This paper is well organized and the experiments are explicitly presented. Therefore, I think the work of this paper is very interesting and... | SP:7a0df8dec9ab3e72c6daea7ed2288d1c59b6d2ec |
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees | 1 INTRODUCTION . As one of the most classic combinatorial optimization problems , the 3D bin packing problem usually refers to packing a set of cuboid-shaped items i ∈ I , with sizes sxi , s y i , s z i along x , y , z axes , respectively , into the minimum number of bins with sizes Sx , Sy , Sz , in an axis-aligned fa... | This work proposes a tree-based learning method for online 3D packing problem. Packing configuration tree nodes is constructed using heuristic-based tree expansion, which acts as the action space of deep reinforcement learning. The tree search schema is interesting, but this work still has lots of space to improve in t... | SP:ab030a8c07a5d9c3e0fa0a6b7cf224b4c23f68d1 |
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees | 1 INTRODUCTION . As one of the most classic combinatorial optimization problems , the 3D bin packing problem usually refers to packing a set of cuboid-shaped items i ∈ I , with sizes sxi , s y i , s z i along x , y , z axes , respectively , into the minimum number of bins with sizes Sx , Sy , Sz , in an axis-aligned fa... | This paper addresses the problem of online 3D bin packing where the order of objects is out of the model's control and it must make placement decisions one object at a time. Training is framed as a deep RL problem closely following recent work [Zhao et al]. The main contribution is a rethinking of the state and action ... | SP:ab030a8c07a5d9c3e0fa0a6b7cf224b4c23f68d1 |
Deep Fair Discriminative Clustering | 1 INTRODUCTION . Clustering is essential as it is the basis of many AI tools and has been widely used in real-world applications involving humans ( Jain et al. , 1999 ) such as market research , social network analysis , and crime analysis . However , as AI tools augment and even replace humans in decision-making , the... | This paper proposes a method for fair clustering (where a clustering is considered fair when each protected group is present in every cluster in the same proportion as in the population) using deep neural networks. The method works by training a neural network for clustering using the deep clustering with virtual adve... | SP:055233a234a97d1e4a37a02b1740303eb41a3dc5 |
Deep Fair Discriminative Clustering | 1 INTRODUCTION . Clustering is essential as it is the basis of many AI tools and has been widely used in real-world applications involving humans ( Jain et al. , 1999 ) such as market research , social network analysis , and crime analysis . However , as AI tools augment and even replace humans in decision-making , the... | This paper proposes a fair clustering algorithm that uses DL models to map the data into deep representations. The authors also show the equivalence between the practical fairness measure and the balance measure. The algorithms can be concluded as two steps: 1) find fair assignments $\hat y$ based on $y$; 2) tune the ... | SP:055233a234a97d1e4a37a02b1740303eb41a3dc5 |
Structure by Architecture: Disentangled Representations without Regularization | 1 INTRODUCTION . Deep learning has achieved strong results on a plethora of challenging tasks . However , performing well on a highly specific dataset is usually insufficient to satisfactorily solve real-world problems ( Tan et al. , 2018 ; Zhuang et al. , 2019 ) . This has lead to a particular interest in consistently... | This paper structures how latents are used in an autoencoder to improve its performance. They are motivated by causal structure and independence of latent variables. They also sample using a sort of discrete mixup between latent codes from pushing forward data, and show various improvements there. | SP:249f8a78affc76e690b74ebe05057bb1c30df872 |
Structure by Architecture: Disentangled Representations without Regularization | 1 INTRODUCTION . Deep learning has achieved strong results on a plethora of challenging tasks . However , performing well on a highly specific dataset is usually insufficient to satisfactorily solve real-world problems ( Tan et al. , 2018 ; Zhuang et al. , 2019 ) . This has lead to a particular interest in consistently... | This paper presents a hierarchical latent variable model and accompanying sampling procedure for learning disentangled representations. Rather than a latent feature map/vector, the latent variables are used to condition affine transforms in the decoder. The authors combine this with a ‘hybrid’ sampling strategy, effect... | SP:249f8a78affc76e690b74ebe05057bb1c30df872 |
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots | 1 INTRODUCTION . Humans have a remarkable ability to efficiently learn to perform tasks by watching others demonstrate similar tasks . For example , children quickly learn the skills needed to play a new sport by watching their parents perform skills such as kicking a ball . Notably , they are able to learn from these ... | The paper proposed to learn skills correspondences between robots of different morphologies in an unsupervised way - without requiring paired data from the robots. They learn a skill translation model that maps skills from a source robot to a target robot. The translation model is learned by minimizing a loss composed ... | SP:3b90d1be98c9d3d60ce7ef415b471a824076b967 |
Translating Robot Skills: Learning Unsupervised Skill Correspondences Across Robots | 1 INTRODUCTION . Humans have a remarkable ability to efficiently learn to perform tasks by watching others demonstrate similar tasks . For example , children quickly learn the skills needed to play a new sport by watching their parents perform skills such as kicking a ball . Notably , they are able to learn from these ... | This paper addresses the problem of transferring skills between morphologically different robots. This approach to learning skill correspondences is framed as a problem of matching distributions of sequences of skills across robots. The paper proposes an unsupervised objective, inspired by work in unsupervised machine ... | SP:3b90d1be98c9d3d60ce7ef415b471a824076b967 |
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future | 1 INTRODUCTION . The current COVID-19 pandemic has challenged our response capabilities to large disruptive events , affecting the health and economy of millions of people . A major tool in our response has been forecasting epidemic trajectories , which has provided lead time to policymakers to optimize and plan interv... | The authors deal with the problem of revising previous recorded data and its effect on timeseries predictions. They showcase how revisions in past data, quantified as the backfill error, can introduce a considerable error in predictions. Towards that, they propose a novel deep learning approach, the Back2Future, that r... | SP:b2c42a7bda0971e7fa51ecfd4cdf4cdb895295de |
Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future | 1 INTRODUCTION . The current COVID-19 pandemic has challenged our response capabilities to large disruptive events , affecting the health and economy of millions of people . A major tool in our response has been forecasting epidemic trajectories , which has provided lead time to policymakers to optimize and plan interv... | The authors consider the effects of backfill dynamics--the correction of historical data--on time series prediction. They use COVID-19 forecasting as the motivating application. Both the features (ER visits, hospitalization rate, etc.) and the target (deaths) are subject to revision, and the authors collect a data set ... | SP:b2c42a7bda0971e7fa51ecfd4cdf4cdb895295de |
Linear algebra with transformers | 1 INTRODUCTION . Since their introduction by Vaswani et al . ( 2017 ) , transformers , originally designed for machine translation , were applied to various problems , from text generation ( Radford et al. , 2018 ; 2019 ) to image processing ( Carion et al. , 2020 ) and speech recognition ( Dong et al. , 2018 ) where t... | This paper “Linear algebra with transformers” studies the application of seq2seq transformers to matrix operations. It studies their performance across different encodings of floating point numbers, different sizes of matrices, different operations, and different (synthetic) data distributions. The main findings are th... | SP:b665f2dbd3a30e5a4d3942abc7b8f9d45db41cb8 |
Linear algebra with transformers | 1 INTRODUCTION . Since their introduction by Vaswani et al . ( 2017 ) , transformers , originally designed for machine translation , were applied to various problems , from text generation ( Radford et al. , 2018 ; 2019 ) to image processing ( Carion et al. , 2020 ) and speech recognition ( Dong et al. , 2018 ) where t... | The authors train generic, dense transformers to perform several standard linear algebra computations, ranging from simple tasks like transposition to complex nonlinear tasks such as matrix inversion. They restrict themselves to relatively small matrices due to the practical limits of the dense, quadratic attention mec... | SP:b665f2dbd3a30e5a4d3942abc7b8f9d45db41cb8 |
Multi-Task Processes | 1 INTRODUCTION . Neural Processes ( NPs ) ( Garnelo et al. , 2018b ) are a class of meta-learning methods that model a distribution of functions ( i.e . a stochastic process ) . By considering a task as a function realized from the underlying stochastic process , they can flexibly adapt to various unseen tasks through ... | The paper presents a novel model for multi-task learning with missing data based on Neural Processes (NP). Inter-task correlations are modelled via a shared latent variable. The model has been tested on 1 synthetic and 2 real-world datasets and is experimentally shown to perform better against 4 baselines including 2 n... | SP:2020439f6e52ec592e2fbc454633c1613e8a82c1 |
Multi-Task Processes | 1 INTRODUCTION . Neural Processes ( NPs ) ( Garnelo et al. , 2018b ) are a class of meta-learning methods that model a distribution of functions ( i.e . a stochastic process ) . By considering a task as a function realized from the underlying stochastic process , they can flexibly adapt to various unseen tasks through ... | This work proposes a multi-task learning architecture for neural processes termed the Multi-task process (MTP). The MTP model conditions task-specific latent variables on a global latent variable that is responsible for information sharing between the tasks, and is able to handle both the isotopic and the heterotopic c... | SP:2020439f6e52ec592e2fbc454633c1613e8a82c1 |
Reverse Engineering of Imperceptible Adversarial Image Perturbations | 1 INTRODUCTION . Deep neural networks ( DNNs ) are susceptible to adversarially-crafted tiny input perturbations during inference . Such imperceptible perturbations , a.k.a . adversarial attacks , could cause DNNs to draw manifestly wrong conclusions . The existence of adversarial attacks was first uncovered in the dom... | The paper considers the problem of automatically reconstructing adversarial perturbations from examples in a post-hoc manner. The authors argue that for an effective reconstruction, it is not sufficient to only minimize the reconstruction error but also it is essential to align the predictions of the original and their... | SP:cf2a87c633ea31f81e59e310d79f3fa0b4c4b031 |
Reverse Engineering of Imperceptible Adversarial Image Perturbations | 1 INTRODUCTION . Deep neural networks ( DNNs ) are susceptible to adversarially-crafted tiny input perturbations during inference . Such imperceptible perturbations , a.k.a . adversarial attacks , could cause DNNs to draw manifestly wrong conclusions . The existence of adversarial attacks was first uncovered in the dom... | This paper proposes a methodology for reverse engineering adversarial perturbations. This allows a defender to recover the original image used to produce an adversarial example and may be an effective tool to mitigating adversarial example attacks. The paper introduces the concept of reverse engineering adversarial pe... | SP:cf2a87c633ea31f81e59e310d79f3fa0b4c4b031 |
rQdia: Regularizing Q-Value Distributions With Image Augmentation | 1 INTRODUCTION . Human perception is invariant to and remarkably robust against many perturbations , like discoloration , obfuscation , and low exposure . On the other hand , artificial neural networks do not intrinsically carry these invariance properties , not without regularizers or hand-crafted inductive biases lik... | Image augmentations have recently become a standard component of deep RL algorithms. Previous work has enforced consistencies at a sample-level. This paper proposes to look at the distribution of statistics at a minibatch-level in order to enforce consistencies. Paper shows results on standard benchmarks in discrete... | SP:21726ae5d1bf57c0e73a5b584475409de91f1214 |
rQdia: Regularizing Q-Value Distributions With Image Augmentation | 1 INTRODUCTION . Human perception is invariant to and remarkably robust against many perturbations , like discoloration , obfuscation , and low exposure . On the other hand , artificial neural networks do not intrinsically carry these invariance properties , not without regularizers or hand-crafted inductive biases lik... | This paper proposes a regularization method for reinforcement learning that encourages the Q-value of the original image (i.e., original state) and the Q-value of the transformed image (new state) to be the same. This method enhances the robustness of RL methods against environment variation. This paper introduces the ... | SP:21726ae5d1bf57c0e73a5b584475409de91f1214 |
Sparse MoEs meet Efficient Ensembles | 1 INTRODUCTION . Neural networks typically use all their parameters to process an input . Sustaining the growth of such models—reaching today up to 100B parameters ( Brown et al. , 2020 ) —is challenging , e.g. , due to their high computational and environmental costs ( Strubell et al. , 2019 ; Patterson et al. , 2021 ... | The authors show empirically that Sparse MOEs and Ensembles have complementary features, and suggest that combining the two should lead to improved performance. Authors build on the Vision Transformer (ViT) for their experiments. To efficiently combine Sparse MOEs and Ensembles, the paper presents Partitioned Batch Ens... | SP:9d6a017b80845249601f40d6e8c4f98cbbe78f56 |
Sparse MoEs meet Efficient Ensembles | 1 INTRODUCTION . Neural networks typically use all their parameters to process an input . Sustaining the growth of such models—reaching today up to 100B parameters ( Brown et al. , 2020 ) —is challenging , e.g. , due to their high computational and environmental costs ( Strubell et al. , 2019 ; Patterson et al. , 2021 ... | The paper investigates the benefits of combining (Sparse) Mixture of Experts (MoE) and ensembling. Sparse MoE’s employ conditional computation to reduce computational and environmental costs of DNNs while maintaining (or increasing) performance. On the other hand, ensembling models has been shown to achieve the highest... | SP:9d6a017b80845249601f40d6e8c4f98cbbe78f56 |
Towards General Robustness to Bad Training Data | 1 INTRODUCTION . The quality of training data is a fundamental ingredient towards useful and reliable ML-based applications . Unfortunately , there are unaccountably many possible data issue types . For example , errors and bias occur frequently in data generation and collection processes . Bad data could also be cause... | This paper proposes, *DataSifter*, an optimization-based, general-purpose framework for filtering "bad data" from a training set. General-purpose broadly covers different data corruption types (e.g., adversarial perturbation, label noise, etc.), different model architectures, and performance metrics (e.g., test error)... | SP:a8937ce7ccd4a5a0daa3ef434aed388abb1ab0d8 |
Towards General Robustness to Bad Training Data | 1 INTRODUCTION . The quality of training data is a fundamental ingredient towards useful and reliable ML-based applications . Unfortunately , there are unaccountably many possible data issue types . For example , errors and bias occur frequently in data generation and collection processes . Bad data could also be cause... | This paper focuses on the problem of identifying bad training data when the underlying cause is unknown in advance. Authors develop an algorithmic framework, DATASIFTER, for general robustness to bad training data. Empirical evaluation show efficacy of DATASIFTER in a wide range of tasks, including backdoor, poison, no... | SP:a8937ce7ccd4a5a0daa3ef434aed388abb1ab0d8 |
FedNAS: Federated Deep Learning via Neural Architecture Search | 1 INTRODUCTION . Federated Learning ( FL ) is a promising approach for decentralized machine learning , which aims to avoid data sharing and lower the communication cost ( McMahan et al. , 2016 ) . As such , it has gained a lot of attention in various domains of machine learning such as computer vision , natural langua... | The authors employ an existing neural architecture search method in the federated learning setting. Specifically, the authors propose FeNAS and extend an existing NAS method MiLeNAS into federated learning to address the data heterogeneity problem and conduct personalization. The experiments show that the proposed me... | SP:9ef06b25ac4a048d02a32376f5ac24e1ea8f546a |
FedNAS: Federated Deep Learning via Neural Architecture Search | 1 INTRODUCTION . Federated Learning ( FL ) is a promising approach for decentralized machine learning , which aims to avoid data sharing and lower the communication cost ( McMahan et al. , 2016 ) . As such , it has gained a lot of attention in various domains of machine learning such as computer vision , natural langua... | This paper combines gradient-based NAS (DARTS-like algorithm: MileNAS) with Federated Learning (FL) setup, to improve both global and personalization performance with learned neural architecture. Since both NAS and FL learning are based on gradient, the extension to FL setup becomes intuitive and effective. Empirically... | SP:9ef06b25ac4a048d02a32376f5ac24e1ea8f546a |
AestheticNet: Reducing bias in facial data sets under ethical considerations | 1 MOTIVATION . In 2016 Beauty.AI , a Hong-Kong based technology company , hosted the first international beauty contest judged by artificial intelligence ( beauty.ai , 2016 ) but the results were heavily biased , for example , against dark skin ( Levin , 2016 ) subjects . “ Machine learning models are prone to biased d... | This paper proposes an AestheticNet and a new approach to bias-free machine learning tools. The former shows a higher Pearson correlation coefficient and a lower mean absolute error than competitive approaches. The latter helps to train an unbiased network with biased data for facial beauty prediction. | SP:3e91cc255d09c478f8da733ba2f45ea40aa2da89 |
AestheticNet: Reducing bias in facial data sets under ethical considerations | 1 MOTIVATION . In 2016 Beauty.AI , a Hong-Kong based technology company , hosted the first international beauty contest judged by artificial intelligence ( beauty.ai , 2016 ) but the results were heavily biased , for example , against dark skin ( Levin , 2016 ) subjects . “ Machine learning models are prone to biased d... | In this paper, the authors study the problem of bias in facial beauty prediction problem. To this end, they first show that there do exist bias in an existing dataset. Then, they show that deep networks trained with such a biased dataset do capture and reflect the bias. Finally, they propose two solutions for addressin... | SP:3e91cc255d09c478f8da733ba2f45ea40aa2da89 |
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction | 1 INTRODUCTION . The ubiquity of graph-structured data and its importance in solving various real-world problems such as node and graph classification have made graph-centered machine learning an important research area ( Lü & Zhou , 2011 ; Shervashidze et al. , 2011 ; Zhu , 2005 ) . Graph neural networks ( GNNs ) off... | This paper develops a self-supervised learning framework to extract node features with the aid of graph. Connections between neighborhood prediction and the XMC problem are also established. Experiments on large-scale data show the superiority of the proposed method. | SP:5e57ecc0e0bf2e70846f7d2c0e9b1b625f5e1f0e |
Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction | 1 INTRODUCTION . The ubiquity of graph-structured data and its importance in solving various real-world problems such as node and graph classification have made graph-centered machine learning an important research area ( Lü & Zhou , 2011 ; Shervashidze et al. , 2011 ; Zhu , 2005 ) . Graph neural networks ( GNNs ) off... | The paper proposed a self-supervised learning framework for learning node feature by exploring the correlation between the node feature and the graph structure, which leverages the graph information based on neighborhood prediction. To be specific, the proposed GIANT approach is combined with the pre-trained language m... | SP:5e57ecc0e0bf2e70846f7d2c0e9b1b625f5e1f0e |
Local Feature Swapping for Generalization in Reinforcement Learning | 1 INTRODUCTION . Advances made in deep learning have opened the way to many applications in computer vision such as classification , object recognition , or image segmentation . The powerful representation capabilities of deep neural networks paved the way for many successes in deep reinforcement learning with the desi... | In this work, a novel regularization method for deep neural networks is introduced. By locally swapping dimensions of intermediate feature maps, the authors report generalization improvements in supervised learning and on several reinforcement learning benchmarks. The method is evaluated against several other common re... | SP:ed090d8999fb0edf10ab2d239f7c0b309c2b9fea |
Local Feature Swapping for Generalization in Reinforcement Learning | 1 INTRODUCTION . Advances made in deep learning have opened the way to many applications in computer vision such as classification , object recognition , or image segmentation . The powerful representation capabilities of deep neural networks paved the way for many successes in deep reinforcement learning with the desi... | This paper proposes a new regularization scheme, which involves randomly permuting nearby (in terms of spatial locations in the downsized feature tensor) depth blocks. The authors show its effectiveness in supervised learning tasks and establish a SOTA-like result over Procgen generalization, and also provide several e... | SP:ed090d8999fb0edf10ab2d239f7c0b309c2b9fea |
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients | 1 INTRODUCTION . Pruning at initialization—where we remove weights from a model before training begins—is a recent and promising area of research that enables us to enjoy the benefits of pruning at training time , and which may aid our understanding of training deep neural networks . Frankle & Carbin ( 2019 ) provide e... | This work studies the problem of pruning neural networks at initialization. It first identifies that the saliency score defined by the existing method SNIP has room for improvement. Specifically, the authors propose a method named prospect pruning to take into account the sequence of weight updates to determine the pru... | SP:b61b07e85d8e940816c1a769cb4c695112f5c7ad |
Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients | 1 INTRODUCTION . Pruning at initialization—where we remove weights from a model before training begins—is a recent and promising area of research that enables us to enjoy the benefits of pruning at training time , and which may aid our understanding of training deep neural networks . Frankle & Carbin ( 2019 ) provide e... | This work focuses on weight pruning at initialization. In this paper, the authors point out an important problem that the pruned subnetwork at initialization is going to be trained and previous prune-at-init methods ignore this fact. As a result, these prune-at-init methods ignore the trainability of weights. This pape... | SP:b61b07e85d8e940816c1a769cb4c695112f5c7ad |
Dissecting Local Properties of Adversarial Examples | 1 INTRODUCTION . Despite deep neural networks ( DNNs ) perform well in many fields ( He et al. , 2016 ; Devlin et al. , 2019 ) , their counter-intuitive vulnerability attracts increasing attention , both for safety-critical applications ( Sharif et al. , 2016 ) and the black-box mechanism of DNNs ( Fazlyab et al. , 201... | This paper studies the properties of adversarial examples from a spatial and frequency perspective and shows that naturally trained models are more vulnerable to high-frequency components in adversarial examples. Perturbations for naturally trained models are disordered, but perturbations for adv-trained models are ima... | SP:d9b458c1ddd8165e4cef7861b7995d80fcf3c434 |
Dissecting Local Properties of Adversarial Examples | 1 INTRODUCTION . Despite deep neural networks ( DNNs ) perform well in many fields ( He et al. , 2016 ; Devlin et al. , 2019 ) , their counter-intuitive vulnerability attracts increasing attention , both for safety-critical applications ( Sharif et al. , 2016 ) and the black-box mechanism of DNNs ( Fazlyab et al. , 201... | This paper provides a set of empirical studies of the spectral and spatial properties of adversarial examples of deep neural nets (DNNs) classifiers. The studies illustrate that standard DNNs are much more sensitive to high frequency components of adversarial examples compared to adversarially-trained DNNs, and also th... | SP:d9b458c1ddd8165e4cef7861b7995d80fcf3c434 |
Curriculum learning as a tool to uncover learning principles in the brain | We present a novel approach to use curricula to identify principles by which a system learns . Previous work in curriculum learning has focused on how curricula can be designed to improve learning of a model on particular tasks . We consider the inverse problem : what can a curriculum tell us about how a learning syste... | This paper simulates simple RNNs performing two classic decision-neuroscience experiments (a free choice evidence accumulation task and a delayed decision evidence accumulation task). The paper examines learning behaviour of these networks under three hand-crafted curricula, for each of two different RNN loss functions... | SP:6a87b089a5efc03251112b2b8551df432c7efb42 |
Curriculum learning as a tool to uncover learning principles in the brain | We present a novel approach to use curricula to identify principles by which a system learns . Previous work in curriculum learning has focused on how curricula can be designed to improve learning of a model on particular tasks . We consider the inverse problem : what can a curriculum tell us about how a learning syste... | In this paper, the authors propose an approach using curricula to identify how a system has learned. Using two commonly used tasks in neuroscience: evidence accumulation and delayed decision recurrent neural networks (RNNs) are trained using two different loss functions (target based and representation based). They sho... | SP:6a87b089a5efc03251112b2b8551df432c7efb42 |
When in Doubt, Summon the Titans: A Framework for Efficient Inference with Large Models | Scaling neural networks to “ large ” sizes , with billions of parameters , has been shown to yield impressive results on many challenging problems . However , the inference cost incurred by such large models often prevent their application in most real-world settings . In this paper , we propose a two-stage framework b... | The paper proposes a two-stage distillation framework to improve inference efficiency and reduce the dependency on large teacher models. The goal of this framework is to only use the large/teacher model for difficult and rare examples and to use the student, smaller model for the more frequent easy examples. The proced... | SP:26369f7db26705f4f172a38ba17240f9a32e5c0d |
When in Doubt, Summon the Titans: A Framework for Efficient Inference with Large Models | Scaling neural networks to “ large ” sizes , with billions of parameters , has been shown to yield impressive results on many challenging problems . However , the inference cost incurred by such large models often prevent their application in most real-world settings . In this paper , we propose a two-stage framework b... | This paper studies efficient inference problem for large models. It proposes to train a small student model, and performs inference for easy data on the student model, and for hard data on the original large model. Experiments show the proposed method performs better than the simple baseline of standard distillation mo... | SP:26369f7db26705f4f172a38ba17240f9a32e5c0d |
Adaptive Learning of Tensor Network Structures | 1 INTRODUCTION . Matrix factorization is ubiquitous in machine learning and data science and forms the backbone of many algorithms . Tensor decomposition techniques emerged as a powerful generalization of matrix factorization . They are particularly suited to handle high-dimensional multi-modal data and have been succe... | First, the idea of rank incremental method for Tensor network decomposition and determination of TN structures is not novel. Second, decomposition with weight transfer is obvious and widely used in the rank incremental method. For generalized tensor networks decomposition, the authors should consider the works. S. ... | SP:ecb271a825f6d8cbc5c045436d6bc937a735e6a1 |
Adaptive Learning of Tensor Network Structures | 1 INTRODUCTION . Matrix factorization is ubiquitous in machine learning and data science and forms the backbone of many algorithms . Tensor decomposition techniques emerged as a powerful generalization of matrix factorization . They are particularly suited to handle high-dimensional multi-modal data and have been succe... | In this paper, a greedy algorithm that can find a structure of a certain class of tensor networks is proposed. The algorithm consists of bi-level optimization, where tensor network structure is optimized in the outer loop and tensor decomposition is computed to approximate a given tensor in the inner loop. The tradeoff... | SP:ecb271a825f6d8cbc5c045436d6bc937a735e6a1 |
Learning Continuous Environment Fields via Implicit Functions | 1 INTRODUCTION . Scene understanding aims to analyze and interpret a given environment . The past few years have witnessed tremendous success in scene representation learning for semantic segmentation ( Long et al. , 2015 ; Li et al. , 2017 ) , 3D scene reconstruction ( Sitzmann et al. , 2020 ; 2019 ) , and depth estim... | The paper proposes modeling reaching distance between any start position and any goal (subject to obstacle avoidance) with a neural network. This is equivalent to parameterizing a traditional path-planning (goal-reaching) continuous value function with the network, which the authors also mention in the introduction sec... | SP:e0e860e28dac58b373db0b14d5c84f7258d2713a |
Learning Continuous Environment Fields via Implicit Functions | 1 INTRODUCTION . Scene understanding aims to analyze and interpret a given environment . The past few years have witnessed tremendous success in scene representation learning for semantic segmentation ( Long et al. , 2015 ; Li et al. , 2017 ) , 3D scene reconstruction ( Sitzmann et al. , 2020 ; 2019 ) , and depth estim... | This work introduces a novel scene representation for agent navigation in 2D and 3D environments. At the core of the method is an implicit neural representation of environment - implicit environment field (IEF) - which is a neural net that maps location coordinates to its reaching distance. Several conditional variants... | SP:e0e860e28dac58b373db0b14d5c84f7258d2713a |
Exploring unfairness in Integrated Gradients based attribution methods | Numerous methods have attempted to explain and interpret predictions made by machine learning models in terms of their inputs . Known as “ attribution methods ” they notably include the Integrated Gradients method and its variants . These are based upon the theory of Shapley Values , a rigorous method of fair allocatio... | This paper explores the characteristics of the method, Integrated Gradients, as an attribution method, that has been proposed to explain black box models. “Baselines” in analyzing integrated gradients are discussed and the shortcomings of integrated gradients are further evaluated. The paper then proposes Integrated Ce... | SP:fd78538153f7e878193f7c975baf7e43baca1a70 |
Exploring unfairness in Integrated Gradients based attribution methods | Numerous methods have attempted to explain and interpret predictions made by machine learning models in terms of their inputs . Known as “ attribution methods ” they notably include the Integrated Gradients method and its variants . These are based upon the theory of Shapley Values , a rigorous method of fair allocatio... | In this paper, the authors analyze the fairness of Integrated Gradient-based attribution methods. They exploit SHAP and BShap, two approaches based on the theory of Shapley Values, as the reference of "fair" methods. Specifically, they present an "attribution transfer" phenomenon in which the Integrated Gradients are... | SP:fd78538153f7e878193f7c975baf7e43baca1a70 |
Missingness Bias in Model Debugging | 1 INTRODUCTION . Model debugging aims to diagnose a model ’ s failures . For example , researchers can identify global biases of models via the extraction of human-aligned concepts ( Bau et al. , 2017 ; Wong et al. , 2021 ) , or understand the texture bias by analyzing the models performance on synthetic datasets ( Gei... | The authors focus on the problem of model debugging (for image recognition). They identify that the proposed tools (that rely on CNNs and ResNets) might suffer from the ‘missingness’ issue, i.e., the absence of features due to masking objects of interest. The authors exhibit how the method of masking pixels/patches can... | SP:ea3b33a6e4cb41f8a77e08f7e72bcf9ea56bec71 |
Missingness Bias in Model Debugging | 1 INTRODUCTION . Model debugging aims to diagnose a model ’ s failures . For example , researchers can identify global biases of models via the extraction of human-aligned concepts ( Bau et al. , 2017 ; Wong et al. , 2021 ) , or understand the texture bias by analyzing the models performance on synthetic datasets ( Gei... | The paper discusses that it is common in Computer Vision debugging and explainability techniques to remove image regions to attribute different regions of the image to the decision of a classification model. Although such removal (of words) can be beneficial for Natural Language model debugging, it adds an additional b... | SP:ea3b33a6e4cb41f8a77e08f7e72bcf9ea56bec71 |
Predicting Physics in Mesh-reduced Space with Temporal Attention | 1 INTRODUCTION . There has been an increasing interest in many scientific disciplines , from computational fluid dynamics [ 3 , 39 ] over graphics [ 42 , 40 ] to quantum mechanics [ 20 , 1 ] , to accelerate numerical simulation using learned models . In particular , methods based on Graph Neural Networks ( GNN ) have s... | This work proposes a new algorithm combining graph-neural-network (GNN) and auto-regressive sequence models for physics prediction problems. The authors first use GNNs to compress the physical graphs, then use transformers to predict the next steps of the compressed representations, and finally use GNNs to recover the ... | SP:95db394e10cd433a8283269953ca17e8b8f46879 |
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