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PROMISSING: Pruning Missing Values in Neural Networks | 1 INTRODUCTION . Missing and incomplete data are abundant in real-world problems ; however , the learning and inference procedures in machine learning ( ML ) models highly rely on high-quality and complete data . Therefore , it is necessary to develop new methods to deal with data imperfections in rugged environments .... | In this paper, the authors propose a method titled PROMISSING; this provides a new approach to handling missing data. Rather than imputation, a complete-case analysis, or inverse probability weighting, among other methods, the authors advocate for learning a problem-specific numerical representation for unknowns. The a... | SP:11a1972c3e8ea1c2dda4776b0d751fd47300ae29 |
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes | 1 INTRODUCTION . Determinantal Point Processes ( DPPs ) were first introduced in the context of quantum mechanics ( Macchi , 1975 ) and have subsequently been extensively studied with applications in several areas of pure and applied mathematics like graph theory , combinatorics , random matrix theory ( Hough et al. , ... | This paper studies the online inference and learning problems for nonsymmetric determinantal point processes (NDPPs). The authors use the online greedy algorithm for MAP inference and modify the learning objective for being suitable in the online setting. Experiments with real-world datasets show that the proposed onli... | SP:543adf5a6e83b2d343b5f4482f1fb41388f1314c |
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes | 1 INTRODUCTION . Determinantal Point Processes ( DPPs ) were first introduced in the context of quantum mechanics ( Macchi , 1975 ) and have subsequently been extensively studied with applications in several areas of pure and applied mathematics like graph theory , combinatorics , random matrix theory ( Hough et al. , ... | This paper proposes online and streaming algorithms for MAP inference and learning for nonsymmetric determinantal point processes (NDPPs). For the streaming setting, data points arrive in an arbitrary order, and the algorithms are constrained to using a single pass over the data, along with requiring sublinear memory ... | SP:543adf5a6e83b2d343b5f4482f1fb41388f1314c |
Reducing the Communication Cost of Federated Learning through Multistage Optimization | 1 INTRODUCTION . In federated learning ( FL ) ( McMahan et al. , 2017 ; Kairouz et al. , 2019 ; Li et al. , 2020 ) , distributed clients interact with a central server to learn a model without directly sharing their data with the server . The training objective is to solve the following minimization problem : min x [ F... | This paper introduces the multistage optimization technique for federated learning applications. Specifically, multistage optimization first uses federated optimization algorithms like FedAvg and SCAFFOLD and converges to some budget, and then uses minibatch algorithms like SGD or accelerated SGD in order to converge f... | SP:2335d2a4b9c3c1bb740563cee4bf529f32400772 |
Reducing the Communication Cost of Federated Learning through Multistage Optimization | 1 INTRODUCTION . In federated learning ( FL ) ( McMahan et al. , 2017 ; Kairouz et al. , 2019 ; Li et al. , 2020 ) , distributed clients interact with a central server to learn a model without directly sharing their data with the server . The training objective is to solve the following minimization problem : min x [ F... | It is known that if the level of heterogeneity is sufficiently high, then accelerated minibatch SGD is optimal for federated optimization matching the known lower bound of (Woodworth et al., 2020a). On the other hand, when the level of heterogeneity is very low, then FedAvg/LocalSGD outperforms the former in terms of c... | SP:2335d2a4b9c3c1bb740563cee4bf529f32400772 |
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation | 1 INTRODUCTION . Deep neural networks ( DNNs ) are widely known to be vulnerable to adversarial examples ( Szegedy et al. , 2013 ; Goodfellow et al. , 2015 ) , i.e. , a human-imperceptible perturbation can lead to misclassification . In adversarial machine learning , the term threat model defines the rules of the attac... | The proposed approach involves creating adversarial examples in a training-free manner by manipulating the frequency components of the image. High -frequency information is used from simple geometric patterns and combined with low-frequency component from the input to create a hybrid image. The resultant image is shown... | SP:7552f3439cddd41bcf5b2c8f6f563558e07dfd5e |
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation | 1 INTRODUCTION . Deep neural networks ( DNNs ) are widely known to be vulnerable to adversarial examples ( Szegedy et al. , 2013 ; Goodfellow et al. , 2015 ) , i.e. , a human-imperceptible perturbation can lead to misclassification . In adversarial machine learning , the term threat model defines the rules of the attac... | The authors propose a new method for generating adversarial images for image classifier. This is a No-Box attack named Hybrid Image Transformation (HIT) or hit attack which is both model free and data-free (no training required). In the experiment section the authors show the efficacy of the proposed attack on the Imag... | SP:7552f3439cddd41bcf5b2c8f6f563558e07dfd5e |
RelaxLoss: Defending Membership Inference Attacks without Losing Utility | As a long-term threat to the privacy of training data , membership inference attacks ( MIAs ) emerge ubiquitously in machine learning models . Existing works evidence strong connection between the distinguishability of the training and testing loss distributions and the model ’ s vulnerability to MIAs . Motivated by ex... | The study tackles the problem of defense against membership inference attack, with a focus on (1) decreasing the performance of the attack, (2) maintaining the classifier’s performance, (3) assuming the blindness towards the attack model. They achieve (1) by closing the distance between the train and test distributions... | SP:fe1017ead727444727d5b16195f6c7d3babf1931 |
RelaxLoss: Defending Membership Inference Attacks without Losing Utility | As a long-term threat to the privacy of training data , membership inference attacks ( MIAs ) emerge ubiquitously in machine learning models . Existing works evidence strong connection between the distinguishability of the training and testing loss distributions and the model ’ s vulnerability to MIAs . Motivated by ex... | The paper proposes a new training algorithm to defend against membership inference attacks (MIA) in machine learning models. Motivated by the connection between MIA success and difference between training and test loss distributions, the proposed algorithm sets a positive target mean training loss value and applies gra... | SP:fe1017ead727444727d5b16195f6c7d3babf1931 |
Maximum Likelihood Estimation for Multimodal Learning with Missing Modality | 1 INTRODUCTION . Multimodal learning is an important research area , which builds models to process and relate information between different modalities ( Ngiam et al. , 2011 ; Srivastava & Salakhutdinov , 2014 ; Baltrušaitis et al. , 2018 ) . Compared with unimodal learning , multimodal learning can achieve better perf... | The authors propose a probabilistic framework to improve the classification accuracy in instances when there exists missing data in the multi-modality datasets (where one of the modalities is the predictive label; however, this label is not assumed missing). To this end, they propose a generalized softmax function as t... | SP:8396c93d47a3bad4245917bc5d84713c9c6fa039 |
Maximum Likelihood Estimation for Multimodal Learning with Missing Modality | 1 INTRODUCTION . Multimodal learning is an important research area , which builds models to process and relate information between different modalities ( Ngiam et al. , 2011 ; Srivastava & Salakhutdinov , 2014 ; Baltrušaitis et al. , 2018 ) . Compared with unimodal learning , multimodal learning can achieve better perf... | This submission proposed a maximum likelihood estimation framework combined with a generalized softmax function to resolve multimodal emotion recognition with missing modality. Two emotion recognition datasets are used in experiments to make comparison with several baseline methods. The results suggest that the propose... | SP:8396c93d47a3bad4245917bc5d84713c9c6fa039 |
Lottery Image Prior | 1 INTRODUCTION . Background Deep neural networks ( DNNs ) , in particular convolutional neural networks ( CNNs ) , have been powerful tools for solving various image inverse problems such as denoising ( Zhang et al. , 2017 ; Guo et al. , 2019 ; Lehtinen et al. , 2018 ) , inpainting ( Pathak et al. , 2016 ; Yu et al. , ... | Summary This submission studies lottery ticket hypothesis for deep neural network based inverse imaging. It considers two scenarios: 1-inference for compressed sensing based on pre-trained deep generative models, 2-deep image prior where all network parameters are fit to a single image. Both scenarios are dealing with ... | SP:ee3c5ca2ee8cb1876ff593863745050eb7e9f941 |
Lottery Image Prior | 1 INTRODUCTION . Background Deep neural networks ( DNNs ) , in particular convolutional neural networks ( CNNs ) , have been powerful tools for solving various image inverse problems such as denoising ( Zhang et al. , 2017 ; Guo et al. , 2019 ; Lehtinen et al. , 2018 ) , inpainting ( Pathak et al. , 2016 ; Yu et al. , ... | This paper researches the lottery ticket hypothesis for networks as a deep image prior or deep generative prior. The specific approach is to (1) train deep networks to reconstruct multiple images for DIP (Ticket finding objectives), (2) conduct iterative magnitude pruning to the trained network, (3) obtain the pruned ma... | SP:ee3c5ca2ee8cb1876ff593863745050eb7e9f941 |
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks | 1 INTRODUCTION . Spiking neural networks ( SNNs ) are brain-inspired models that transmit spikes between neurons for event-driven energy-efficient computation . SNNs can be implemented with less energy on neuromorphic hardware ( Akopyan et al. , 2015 ; Davies et al. , 2018 ; Pei et al. , 2019 ; Roy et al. , 2019 ) , wh... | In this paper, the authors proposed a method to train Spiking Neural Networks (SNN) with spike-based implicit differentiation on the equilibrium state. Main idea is to use a spike-triggered event instead of average firing rate to approximate implicit differentiation of Feedback Spiking Neural Networks (FSNN). To enable... | SP:5456e53f2d1a0f4eda6dddc67ea65cb23cee6216 |
SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networks | 1 INTRODUCTION . Spiking neural networks ( SNNs ) are brain-inspired models that transmit spikes between neurons for event-driven energy-efficient computation . SNNs can be implemented with less energy on neuromorphic hardware ( Akopyan et al. , 2015 ; Davies et al. , 2018 ; Pei et al. , 2019 ; Roy et al. , 2019 ) , wh... | The paper aims at porting the IDE method into a spike-fbased and more bio-plausible version. The previous IDE used firing rates rather than spikes for computation, although reference Xiao et al in NeurIPS 2021 had already addressed implementations in spiking neural networks. The authors analyze the approximation error... | SP:5456e53f2d1a0f4eda6dddc67ea65cb23cee6216 |
Benchmarking Sample Selection Strategies for Batch Reinforcement Learning | 1 INTRODUCTION . A key question in machine learning is to select the suitable training samples ( Katharopoulos & Fleuret , 2018 ) . Many prior works proved that an appropriate sample selection strategy , i.e. , removing redundant data or selecting samples according to their hardness , usually significantly improves the... | This paper empirically studies six variants of prioritized experience replay, typically used in online RL, in a batch RL setting. The comparison is performed using TD3BC on three D4RL Mujoco benchmark environment times 5 data sets. The experiments study the performance and bootstrapping errors. Among other things, it i... | SP:b2e35fe3a80f221b5a384f2a2ce66abd92275d63 |
Benchmarking Sample Selection Strategies for Batch Reinforcement Learning | 1 INTRODUCTION . A key question in machine learning is to select the suitable training samples ( Katharopoulos & Fleuret , 2018 ) . Many prior works proved that an appropriate sample selection strategy , i.e. , removing redundant data or selecting samples according to their hardness , usually significantly improves the... | This paper investigated the effect of non-uniform sampling in an offline RL setting. Using TD3BC (Fujimoto and Gu, 2021) as a backbone offline RL algorithm, the authors applied prioritized experience replay (PER) to the sampling of TD3AC with variants of priority metric, including standard TD error, rank-based return, ... | SP:b2e35fe3a80f221b5a384f2a2ce66abd92275d63 |
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View | 1 INTRODUCTION . Disentangled representation learning aims to identify and decompose the underlying explanatory factors hidden in the observed data , which is believed by many to be the only way to understand the world for AI fundamentally ( Bengio & LeCun , 2007 ) . To achieve the goal , as shown in Figure 1 ( a ) , w... | This paper presents a framework to model disentangled directions for pretrained models. Such an approach mitigates the problems with poor generation quality arising while training models with additional regularization terms to force disentanglement. The underlying idea is contrastive-based: similar image variations are... | SP:e547b90d039328d391756b0657f9653e1a5c2d2b |
Learning Disentangled Representation by Exploiting Pretrained Generative Models: A Contrastive Learning View | 1 INTRODUCTION . Disentangled representation learning aims to identify and decompose the underlying explanatory factors hidden in the observed data , which is believed by many to be the only way to understand the world for AI fundamentally ( Bengio & LeCun , 2007 ) . To achieve the goal , as shown in Figure 1 ( a ) , w... | The paper proposes a novel representation learning technique to disentangle the latent space of pre-trained generative models, by discovering semantically meaningful directions in them. The method trains a navigator and a delta-contrastor network, which consists of 2 encoders sharing weights. First, random samples are... | SP:e547b90d039328d391756b0657f9653e1a5c2d2b |
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis | 1 INTRODUCTION . Seizures are among the most common neurological emergencies in the world ( Strein et al. , 2019 ) . Seizures can be chronic as in the case of epilepsy , a neurological disease affecting 50 million people worldwide ( WHO , 2019 ) . Clinically , definitive detection of a seizure is only the first step in... | The paper presents a method for seizure detection and classification. In particular, the method is self supervised, based on graph neural network and use EEG signals. The authors report significant performance in detection and classification, as well as provide methods for qualitative evaluation of model interpretabili... | SP:4f9202a08ea9a0a243b07b0536906deca67f2391 |
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis | 1 INTRODUCTION . Seizures are among the most common neurological emergencies in the world ( Strein et al. , 2019 ) . Seizures can be chronic as in the case of epilepsy , a neurological disease affecting 50 million people worldwide ( WHO , 2019 ) . Clinically , definitive detection of a seizure is only the first step in... | The authors propose a graph-based representation from thresholded Gaussian and linear (correlation) kernels (undirected connectivity) coupled with a diffusion convolutional recurrent network. Besides, a Fourier-based preprocessing is carried out with self-supervised (autoencoders) to initialize the network weights. E... | SP:4f9202a08ea9a0a243b07b0536906deca67f2391 |
Quantized sparse PCA for neural network weight compression | 1 INTRODUCTION . Deep neural networks have achieved state-of-the-art results in a wide variety of tasks . However , deployment remains challenging due to their large compute and memory requirements . Neural networks deployed on edge devices such as mobile or IoT devices are subject to stringent compute and memory const... | This paper introduces a novel method of weight compression. Weight tensors are stored as sparse, quantized matrix factors, and the underlying matrix factorization problem can be considered as a quantized sparse PCA problem and be solved through iterative projected gradient descent methods. The authors' method is appli... | SP:8b45533993822064150ae1adb3900d48ad87b2fb |
Quantized sparse PCA for neural network weight compression | 1 INTRODUCTION . Deep neural networks have achieved state-of-the-art results in a wide variety of tasks . However , deployment remains challenging due to their large compute and memory requirements . Neural networks deployed on edge devices such as mobile or IoT devices are subject to stringent compute and memory const... | The paper proposes a method for compression of neural network weights. The proposal turns weight tensors into matrices, factorizes these matrices into a rank-k factorization via PCA, applies quantization to the factor matrices, and additionally makes the right (latent) matrix sparse. An algorithm is presented, with two... | SP:8b45533993822064150ae1adb3900d48ad87b2fb |
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning | 1 INTRODUCTION . This paper studies online sequential decision making problems such as bandits and reinforcement learning ( RL ) subject to a conservative constraint . Specifically , the agent is given a reliable baseline policy that may not be optimal but still satisfactory . In conservative bandits and RL , the agent... | This paper proposes a reduction-based framework for a large class of reinforcement learning algorithms, including bandits, linear bandits, tabular MDP and linear MDP. The authors notably propose a generic lower bound that holds for all the studied class of algorithms. The lower bound is built on the regret decompositio... | SP:adb7cbd7634b46d7388ac172ef3fbb03c89e6188 |
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning | 1 INTRODUCTION . This paper studies online sequential decision making problems such as bandits and reinforcement learning ( RL ) subject to a conservative constraint . Specifically , the agent is given a reliable baseline policy that may not be optimal but still satisfactory . In conservative bandits and RL , the agent... | This paper studies bandits and RL settings subject to a conservative constraint where the agent has to perform at least as well as a given baseline policy. It improves the existing lower bound for conservative MAB, and as the main contribution, obtains new lower bounds for conservative linear bandits, tabular RL and lo... | SP:adb7cbd7634b46d7388ac172ef3fbb03c89e6188 |
Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-Level Backdoor Attacks | 1 INTRODUCTION . Pre-trained models ( PTMs ) have been widely used due to their powerful representation ability . In the pre-training-then-fine-tuning paradigm , practitioners usually download PTMs , such as BERT ( Devlin et al. , 2019 ) and VGGNet ( Simonyan & Zisserman , 2015 ) , from public sources and fine-tune the... | This paper shows that a backdoored pre-trained model can behave maliciously in various downstream tasks without foreknowing task information. Instead of building up connections between triggers and target labels, this paper explores to assign predefined output representations to triggers. Also, to avoid all triggers ca... | SP:db8ceeba535e0a4d0102ce512d9db4e53fc8971f |
Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-Level Backdoor Attacks | 1 INTRODUCTION . Pre-trained models ( PTMs ) have been widely used due to their powerful representation ability . In the pre-training-then-fine-tuning paradigm , practitioners usually download PTMs , such as BERT ( Devlin et al. , 2019 ) and VGGNet ( Simonyan & Zisserman , 2015 ) , from public sources and fine-tune the... | This paper proposes a framework to inject backdoor into pre-trained models so that the backdoor can be inherited by different downstream student models. The key part of the attack is to restrict the output representations of backdoor samples via a proposed loss function. Experiment results show that the proposed method... | SP:db8ceeba535e0a4d0102ce512d9db4e53fc8971f |
Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling | 1 INTRODUCTION . Humans are amazing at extracting knowledge efficiently from our complicated and multimodal world , leveraging both redundant and complementary information from visual , acoustic , or tactile cues . Investigating into such behavior , neuroimaging and neuroanatomical studies suggested that specific brain... | This paper presents a new model for grammar induction for text, with help from the coupled images. The model was built on top of an existing unsupervised grammar induction model used for text without image information. The experimental results show the approach was effective. The work essentially demonstrates some effe... | SP:deaee5e7a87bf430a6831dde8c2a2c84f62201ef |
Unsupervised Vision-Language Grammar Induction with Shared Structure Modeling | 1 INTRODUCTION . Humans are amazing at extracting knowledge efficiently from our complicated and multimodal world , leveraging both redundant and complementary information from visual , acoustic , or tactile cues . Investigating into such behavior , neuroimaging and neuroanatomical studies suggested that specific brain... | The paper proposed a new method CLIORA to do unsupervised parsing and vision-language grounding. CLIORA is based on DIORA model. But different from previous unsupervised parsing methods, CLIORA also induces alignment between constituents and image regions. In order to train the model, the author introduces a contras... | SP:deaee5e7a87bf430a6831dde8c2a2c84f62201ef |
A composable autoencoder-based algorithm for accelerating numerical simulations | 1 INTRODUCTION . Numerical solutions to partial differential equations ( PDEs ) are dependent on PDE conditions such as , geometry of the computational domain , boundary conditions , initial conditions and source terms . Commercial PDE solvers have shown a tremendous success in accurately modeling PDEs for a wide range... | The paper proposed CoAE-MLSim to learn with relatively fewer samples of PDE solutions and solve PDEs. CoAE-MLSim uses the idea of domain decomposition: first learn the solution on local subdomains using autoencoder, and then couple these sub-solutions by an iterative algorithm. Numerical experiments are performed to te... | SP:fa33e5a45b74feb3277ec2c9c980719fabd472dd |
A composable autoencoder-based algorithm for accelerating numerical simulations | 1 INTRODUCTION . Numerical solutions to partial differential equations ( PDEs ) are dependent on PDE conditions such as , geometry of the computational domain , boundary conditions , initial conditions and source terms . Commercial PDE solvers have shown a tremendous success in accurately modeling PDEs for a wide range... | This paper proposes a new ML approach called CoAE-MLSim that is a faster alternative to PDE solvers. Compared to previous ML work on this problem, it aims to be more accurate and generalize better across PDE conditions. They also aim to require fewer PDE solutions to train the model. | SP:fa33e5a45b74feb3277ec2c9c980719fabd472dd |
Frame Averaging for Invariant and Equivariant Network Design | 1 INTRODUCTION . Many tasks in machine learning ( ML ) require learning functions that are invariant or equivariant with respect to symmetric transformations of the data . For example , graph classification is invariant to a permutation of its nodes , while node prediction tasks are equivariant to node permutations . C... | The paper proposes to make any neural network equivariant by symmetrizing over a subset of the group, rather than over whole group. If the subset selection F(X), depending on input X, is equivariant (gFX=FgX), then the symmetrization is equivariant. The authors furthermore prove: 1) When interested in invariant predict... | SP:0241b8a73225e20c6d486355f34f267d87ef1f44 |
Frame Averaging for Invariant and Equivariant Network Design | 1 INTRODUCTION . Many tasks in machine learning ( ML ) require learning functions that are invariant or equivariant with respect to symmetric transformations of the data . For example , graph classification is invariant to a permutation of its nodes , while node prediction tasks are equivariant to node permutations . C... | **Summary and Contributions**: The paper introduces a framework called Frame Averaging (FA) that can adapt existing backbone architectures to become invariant/equivariant to new symmetry types. It achieves this by averaging over an input-dependent frame which outputs a subset of groups. Frame averaging is often much m... | SP:0241b8a73225e20c6d486355f34f267d87ef1f44 |
Structured Uncertainty in the Observation Space of Variational Autoencoders | 1 INTRODUCTION Generative modelling is one of the cornerstones of modern machine learning . One of the most used and widespread classes of generative models is the Variational Autoencoder ( VAE ) ( Kingma & Welling , 2014 ; 2019 ) . VAEs explicitly model the distribution of observations by assuming a latent variable mo... | The authors aim at improving the canonical VAE model by replacing the standard iid Gaussian likelihood with a multivariate Gaussian with (low-rank + diagonal) covariance. In applications to CelebA and a brain MRI dataset from UK Biobank, the authors compare the proposed structured-observation-space VAE with a canonica... | SP:29f19f93648edfbc8c30536e8e99a4437c560993 |
Structured Uncertainty in the Observation Space of Variational Autoencoders | 1 INTRODUCTION Generative modelling is one of the cornerstones of modern machine learning . One of the most used and widespread classes of generative models is the Variational Autoencoder ( VAE ) ( Kingma & Welling , 2014 ; 2019 ) . VAEs explicitly model the distribution of observations by assuming a latent variable mo... | In the standard Variational Autoencoder framework the statistics of the decoder output are assumed to be pixel-independent Gaussian which can lead to problems when sampling from the model when covariances are missing. To overcome these limitations, the authors propose to use the network architecture proposed by Monteir... | SP:29f19f93648edfbc8c30536e8e99a4437c560993 |
Learning to Complete Code with Sketches | 1 INTRODUCTION . Recent high-capacity language models ( LM ) have shown that machine learning models are able to generate coherent , realistic text , but it is often hard to guide them towards a specific goal , especially when describing the intent is complex or more costly than manually generating the target output . ... | This work proposes, GRAMMAFORMER, a transformer model for generating code with "holes" inserted in places where a model is uncertain. GRAMMAFORMER is trained on code completion task for C# and Python. The model generates 10-50% more accurate completions and 37-50% longer sketches. | SP:44bc7dda97cc764219656e8a9a6cc8f60d195c29 |
Learning to Complete Code with Sketches | 1 INTRODUCTION . Recent high-capacity language models ( LM ) have shown that machine learning models are able to generate coherent , realistic text , but it is often hard to guide them towards a specific goal , especially when describing the intent is complex or more costly than manually generating the target output . ... | The paper presents a new model for code completion which allows the model to completions with “holes” that are inserted in places where the model is uncertain. The idea of generating “holes” to enable skipping over “hard parts” of the prediction is novel and interesting. To realize this idea, the authors present a mode... | SP:44bc7dda97cc764219656e8a9a6cc8f60d195c29 |
$G^3$: Representation Learning and Generation for Geometric Graphs | A geometric graph is a graph equipped with geometric information ( i.e. , node coordinates ) . A notable example is molecular graphs , where the combinatorial bonding is supplement with atomic coordinates that determine the three-dimensional structure . This work proposes a generative model for geometric graphs , capit... | The paper proposes a generative models for learning over the space of geometric graphs -- those graphs whose nodes are associated with geometric coordinates (point clouds). The point cloud serves as basis for decoding graph structure. This makes the entire system efficient. An extensive suite of experiments on chemical... | SP:d713225fa41061a2ad23a072786c21f066b2a777 |
$G^3$: Representation Learning and Generation for Geometric Graphs | A geometric graph is a graph equipped with geometric information ( i.e. , node coordinates ) . A notable example is molecular graphs , where the combinatorial bonding is supplement with atomic coordinates that determine the three-dimensional structure . This work proposes a generative model for geometric graphs , capit... | This paper studies the problem of geometric graph generation (mainly focusing on molecule graph generation). Specifically, the authors propose a new method, namely Geometric Graph Generator (G3), which generates three-dimensional geometric graphs. Different from others, G3 can capture both the combinatorial and the ge... | SP:d713225fa41061a2ad23a072786c21f066b2a777 |
Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness | 1 INTRODUCTION . Deep learning has shown high performance in several tasks such as image recognition , speech recognition , and natural language processing . In particular , convolutional neural networks ( CNNs ) and dilated CNNs have been quite effective in tasks involving high-dimensional data ( van den Oord et al. ,... | The paper studies non-parametric regression for functions defined on infinite-dimensional input data (such as signals in $\ell^2$), using fully-connected networks or dilated convolutional networks (in the CNN case, convolutional layers are followed by a fully-connected network). The authors consider certain smoothness ... | SP:0edea0200b34d109c964bc9b15e5a4dac5578515 |
Learnability of convolutional neural networks for infinite dimensional input via mixed and anisotropic smoothness | 1 INTRODUCTION . Deep learning has shown high performance in several tasks such as image recognition , speech recognition , and natural language processing . In particular , convolutional neural networks ( CNNs ) and dilated CNNs have been quite effective in tasks involving high-dimensional data ( van den Oord et al. ,... | The authors consider approximation and learning by deep neural networks in the setting with an infinite dimensional input space. They provide nice rates for approximating and learning functions with mixed or anisotropic smoothness. The networks studied in the paper include fully connected ReLU networks and those genera... | SP:0edea0200b34d109c964bc9b15e5a4dac5578515 |
HyperTransformer: Attention-Based CNN Model Generation from Few Samples | 1 INTRODUCTION . In few-shot learning , a conventional machine learning paradigm of fitting a parametric model to training data is taken to a limit of extreme data scarcity where entire categories are introduced with just one or few examples . A generic approach to solving this problem uses training data to identify pa... | This work presents the use of a hybrid CNN-Transformer model for few-shot image classification. Specifically, the paper applies encoding and encoding-decoding transformers between convolution layers to increase the learning capacity. The paper also includes a Conv-based hybrid model and uses Omniglot, miniImageNet, an... | SP:bba5c62e1900284fdf028d2ece640157f7cb4c92 |
HyperTransformer: Attention-Based CNN Model Generation from Few Samples | 1 INTRODUCTION . In few-shot learning , a conventional machine learning paradigm of fitting a parametric model to training data is taken to a limit of extreme data scarcity where entire categories are introduced with just one or few examples . A generic approach to solving this problem uses training data to identify pa... | The paper proposes a method for solving few-shot image classification that generates all the weights of a very small CNN model. This has the advantage that the generated model can be very small+compact, compared against for example some embedding methods that might require large image classification networks to run as ... | SP:bba5c62e1900284fdf028d2ece640157f7cb4c92 |
Language-driven Semantic Segmentation | 1 INTRODUCTION . Semantic segmentation is a core problem in computer vision , with the aim of partitioning an image into coherent regions with their respective semantic class labels . Most existing methods for semantic segmentation assume a limited set of semantic class labels that can potentially be assigned to a pixe... | The paper proposes Language driven Semantic segmentation (LSeg) for semantic segmentation. Essentially, LSeg embeds text labels and image pixels into a common space, and assigns the closest label to each pixel. LSeg is flexible and can dynamically handle arbitrary label sets on the fly with varying length, content, and... | SP:c8776bd6d8dba1ccaabefcdab75900df8e709aa5 |
Language-driven Semantic Segmentation | 1 INTRODUCTION . Semantic segmentation is a core problem in computer vision , with the aim of partitioning an image into coherent regions with their respective semantic class labels . Most existing methods for semantic segmentation assume a limited set of semantic class labels that can potentially be assigned to a pixe... | This paper uses the pre-trained large model (CLIP) to transfer the language knowledge to the unseen labels for zero-shot semantic segmentation. The idea is reasonable and coherent with previous works that distill knowledge from pre-trained models. The experiments also show good results on several benchmarks in a zero-s... | SP:c8776bd6d8dba1ccaabefcdab75900df8e709aa5 |
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | 1 INTRODUCTION . Distributed learning within the framework of federated learning ( Konečnỳ et al. , 2016 ; McMahan et al. , 2017 ) has witnessed increasing interest recently . A key property of this framework is that models are trained locally using only private data on devices/machines distributed across a network ,... | This paper proposes two variants of stochastic gradient algorithms without replacement. For smooth functions satisfying the PŁ condition, the authors showed that the proposed shuffling-based variants converge faster than their with-replacement counterparts (for the case with large number of epochs). Moreover, the autho... | SP:d90994dacbd0fd015426f033a721793d90051f0c |
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond | 1 INTRODUCTION . Distributed learning within the framework of federated learning ( Konečnỳ et al. , 2016 ; McMahan et al. , 2017 ) has witnessed increasing interest recently . A key property of this framework is that models are trained locally using only private data on devices/machines distributed across a network ,... | This paper studies two popular algorithms: local SGD and minibatch SGD using the data shuffling technique, which means sampling without replacement. Authors provide analysis under PL condition and show that in some cases these methods with shuffling outperform classical local SGD and minibatch SGD. Additionally, this p... | SP:d90994dacbd0fd015426f033a721793d90051f0c |
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling | 1 INTRODUCTION . Generative models are most useful to creators if they can generate realistic outputs , afford many avenues for control , and easily fit into existing creative workflows ( Huang et al. , 2020 ) . Deep generative models are expressive function approximators , capable of generating realistic samples in ma... | This paper proposes a music performance modeling network using three sub-modules which are expression generator, synthesis generator, and DDSP inference. The idea of using these three sub-modules to create three-level performance modeling (the three-level control values are notes, performance features, and synthesis pa... | SP:4880635544d5dd03261487167b770ca4de40909f |
MIDI-DDSP: Detailed Control of Musical Performance via Hierarchical Modeling | 1 INTRODUCTION . Generative models are most useful to creators if they can generate realistic outputs , afford many avenues for control , and easily fit into existing creative workflows ( Huang et al. , 2020 ) . Deep generative models are expressive function approximators , capable of generating realistic samples in ma... | This paper presents a controllable rendering engine for MIDI files, based on the DDSP framework. Given F0 and loudness contour, DDSP can estimate the parameters of a harmonic + noise synthesis model, to render a corresponding audio file. Similar to MIDI2Params, which predicts framewise FO and loudness contours from a M... | SP:4880635544d5dd03261487167b770ca4de40909f |
Sqrt(d) Dimension Dependence of Langevin Monte Carlo | ( √ d/ ) mixing time bound for LMC , without warm start , under the common log-smooth and log-strongly-convex conditions , plus a growth condition on the 3rd-order derivative of the potential of target measures . This bound improves the best previously known Õ ( d/ ) result and is optimal ( in terms of order ) in both... | The manuscript considers the unadjusted Langevin Monte Carlo (LMC) algorithm, and performs non-asymptotic analysis of its convergence with respect to the 2 Wasserstein distance. The main contribution is a mixing time bound of O(d^0.5/\epilson), which improves upon the existing O(d/\epsilon) bound of Durmus and Moulines... | SP:1811e49ae2fadeeb486fb1058875193137f20675 |
Sqrt(d) Dimension Dependence of Langevin Monte Carlo | ( √ d/ ) mixing time bound for LMC , without warm start , under the common log-smooth and log-strongly-convex conditions , plus a growth condition on the 3rd-order derivative of the potential of target measures . This bound improves the best previously known Õ ( d/ ) result and is optimal ( in terms of order ) in both... | The paper is concerned with the non-asymptotic analysis of SDE-based sampling algorithms and has two main contributions. Firstly, it improves upon the general framework of [Li et al. (2019)](https://proceedings.neurips.cc/paper/2019/hash/7d265aa7147bd3913fb84c7963a209d1-Abstract.html) and, in particular, does not requi... | SP:1811e49ae2fadeeb486fb1058875193137f20675 |
Learning Identity-Preserving Transformations on Data Manifolds | 1 INTRODUCTION . A goal of many machine learning models is to accurately identify objects as they undergo natural transformations – a task that humans are adept at . According to the manifold hypothesis , natural variations in high-dimensional data lie on or near a low-dimensional , nonlinear manifold ( Fefferman et al... | This paper proposes to learn natural transformations in datasets, with manifold auto encoder (MAE), where the underlying identity-preserving transformations are not easily identifiable. By using Lie group operator, this problem reduces to learning paths/motion in the latent space of MAE. The main challenge in training ... | SP:37aab9484f502a34029583b62eeb6657326bd0c3 |
Learning Identity-Preserving Transformations on Data Manifolds | 1 INTRODUCTION . A goal of many machine learning models is to accurately identify objects as they undergo natural transformations – a task that humans are adept at . According to the manifold hypothesis , natural variations in high-dimensional data lie on or near a low-dimensional , nonlinear manifold ( Fefferman et al... | The authors introduce an approach for learning identity preserving transformations from data. First the low-dimensional manifold structure of the data is learned using an autoencoding neural network, then the transformations (the Lie group operators and their coefficients of combination) that map between perceptually s... | SP:37aab9484f502a34029583b62eeb6657326bd0c3 |
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? | 1 INTRODUCTION . Deep neural networks are powerful tools but their success depends strongly on the amount of training data ( Sun et al. , 2017 ; Mahajan et al. , 2018 ) . Recent works show that for improving robust training against adversarial examples ( Biggio et al. , 2013 ; Szegedy et al. , 2014 ; Biggio & Roli , 20... | This paper focuses on utilizing synthetic images generated by a generative model for the task of achieving robustness to adversarial attacks. Towards this, the paper aims to assess the suitability of the proxy distribution defined by the generative model for the underlying task. The paper shows that conditional Wassers... | SP:53652cae401fc0eefd3e6aa57552dac089eb91fc |
Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness? | 1 INTRODUCTION . Deep neural networks are powerful tools but their success depends strongly on the amount of training data ( Sun et al. , 2017 ; Mahajan et al. , 2018 ) . Recent works show that for improving robust training against adversarial examples ( Biggio et al. , 2013 ; Szegedy et al. , 2014 ; Biggio & Roli , 20... | The paper investigates if generative models can be used to improve the robust accuracy of image models. The authors show that the current SOTA across multiple attack models and commonly used datasets can be significantly improved by using generative models. They show that the conditional Wasserstein difference between ... | SP:53652cae401fc0eefd3e6aa57552dac089eb91fc |
NViT: Vision Transformer Compression and Parameter Redistribution | 1 INTRODUCTION . Self-attention based transformer models demonstrate high model capacity , easy scalability , and superior ability in capturing long-range dependency ( Vaswani et al. , 2017 ; Devlin et al. , 2018 ; Radford et al. , 2018 ; Jiao et al. , 2019 ; Brown et al. , 2020 ) . They have thus been widely applied t... | This paper applies latency-aware global structural pruning to vision transformers (ViTs), which results in redistribution of the model parameters and better a speed-accuracy tradeoff. Compared to Deit-B, the pruned vision transformer model (NVP) is 1.85x faster with almost no performance loss. Based on the insights dis... | SP:7def75e4937f11e2c20c7694f54d512a08eb64d6 |
NViT: Vision Transformer Compression and Parameter Redistribution | 1 INTRODUCTION . Self-attention based transformer models demonstrate high model capacity , easy scalability , and superior ability in capturing long-range dependency ( Vaswani et al. , 2017 ; Devlin et al. , 2018 ; Radford et al. , 2018 ; Jiao et al. , 2019 ; Brown et al. , 2020 ) . They have thus been widely applied t... | This paper studies the latency reduction of Vision Transformer model. The proposed pruning method w.r.t importance score is trained with the full pre-trained model using knowledge distillation, with latency aware regularization. In addition, the author designed a new architecture NViT with a parameter redistribution. T... | SP:7def75e4937f11e2c20c7694f54d512a08eb64d6 |
CrowdPlay: Crowdsourcing human demonstration data for offline learning in Atari games | Crowdsourcing has been instrumental for driving AI advances that rely on largescale data . At the same time reinforcement learning has seen rapid progress through benchmark environments that strike a balance between tractability and real-world complexity , such as ALE and OpenAI Gym . In this paper we aim to fill a gap... | This paper presents CrowdPlay, a crowdsourcing platform to collect human demonstrations for any MDP. It also accompanies a dataset of human gameplay on Atari games with multi-agent and some multi-behavior aspects. The paper benchmarks existing offline RL algorithms on this dataset, and details incentive design mechanis... | SP:97bf1da27f21e03aedf82818498273acf15146c9 |
CrowdPlay: Crowdsourcing human demonstration data for offline learning in Atari games | Crowdsourcing has been instrumental for driving AI advances that rely on largescale data . At the same time reinforcement learning has seen rapid progress through benchmark environments that strike a balance between tractability and real-world complexity , such as ALE and OpenAI Gym . In this paper we aim to fill a gap... | This paper proposes a novel framework CrowdPlay for crowdsourcing human data based on standard RL environments. This CrowdPlay pipeline not only supports recruiting different users from different channels to collect multimodal behaviors and data but also designs diverse and real-time incentive mechanisms to guarantee a... | SP:97bf1da27f21e03aedf82818498273acf15146c9 |
S3: Supervised Self-supervised Learning under Label Noise | 1 INTRODUCTION . It is now commonly accepted that supervised learning with deep neural networks can provide excellent solutions for a wide range of problems , so long as there is sufficient availability of labeled training data and computational resources . However , these results have been mostly obtained using well-c... | The paper proposes a two-stage approach to learning with noisy labels (LNL). 1. a. Clean sample selection based on cosine similarity with k nearest neighbors in embedding space: the average of class distribution of those neighbors should be consistent with the label for sample to be selected. b. Noisy sample rela... | SP:22238bda86b9ade5ef7574767f30a6dd644d40c9 |
S3: Supervised Self-supervised Learning under Label Noise | 1 INTRODUCTION . It is now commonly accepted that supervised learning with deep neural networks can provide excellent solutions for a wide range of problems , so long as there is sufficient availability of labeled training data and computational resources . However , these results have been mostly obtained using well-c... | This paper proposes "S3" framework for learning with noisy labels. Specifically, S3 consists of two stages. In the first stage, a relabelling approach and normalized neighboring voting are utilized to guide efficient sample selection; in the second stage, supervised loss (Mixup) and self-consistency loss are used to tr... | SP:22238bda86b9ade5ef7574767f30a6dd644d40c9 |
The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders | 1 INTRODUCTION . Many modern generative models of choice ( e.g . Generative Adversarial Networks ( Goodfellow et al. , 2014 ) , Variational Autoencoders ( Kingma & Welling , 2013 ) ) are modeled as non-linear , possibly stochastic transformations of a simple latent distribution ( e.g . a standard Gaussian ) . A particu... | This purely mathematical paper investigates the important question of how does the necessary level of complexity of an inference subnetwork depend on the the complexity of the corresponding generative subnetwork, in a VAE model. The paper introduces a specific measure of invertibility, and uses it to show that a genera... | SP:8846907bdbc8c93f43abdd4fac5f496a6bc15468 |
The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders | 1 INTRODUCTION . Many modern generative models of choice ( e.g . Generative Adversarial Networks ( Goodfellow et al. , 2014 ) , Variational Autoencoders ( Kingma & Welling , 2013 ) ) are modeled as non-linear , possibly stochastic transformations of a simple latent distribution ( e.g . a standard Gaussian ) . A particu... | The paper answers the question how complex inference models need to be to accurately estimate posterior distributions. The conclusion is when a latent Gaussian model with N parameters satisfies (1) strong invertibility, (2) 3-th smoothness, the posterior can be approximated by a deep latent Gaussian model with O(N) par... | SP:8846907bdbc8c93f43abdd4fac5f496a6bc15468 |
Relational Learning with Variational Bayes | 1 INTRODUCTION . American Psychological Association defines relational learning as ( VandenBos & APA , 2007 ) : Definition 1.1 ( Relational learning ) . Learning to differentiate among stimuli on the basis of relational properties rather than absolute properties . In other words , relational learning refers to the abil... | This paper introduces a variational method for relational learning. It first introduces relational learning as learning based on relational property instead of absolute property, and introduces conditions (Eq. 1). Then it proposes VRL-PGM with a variational lower bound. To eliminate the information short-cut, it introd... | SP:e3a0b2cb1a7e2ed24eb413cbd4545cfcddc30a69 |
Relational Learning with Variational Bayes | 1 INTRODUCTION . American Psychological Association defines relational learning as ( VandenBos & APA , 2007 ) : Definition 1.1 ( Relational learning ) . Learning to differentiate among stimuli on the basis of relational properties rather than absolute properties . In other words , relational learning refers to the abil... | This paper proposes a relational learning method based on variational Bayes. The main idea is to learn relations among objects independently of each object's own properties. The model theory is discusses where authors define the problem and discuss about solutions for some limitations of the model when a relation betwe... | SP:e3a0b2cb1a7e2ed24eb413cbd4545cfcddc30a69 |
The Evolution of Uncertainty of Learning in Games | 1 INTRODUCTION . A primary goal of ML research is to understand the behaviors of learning algorithms in various settings . One standard approach is from each initial condition , we determine whether a learning algorithm converges to a local optimum or stable state . Yet , in the context of online learning in games , an... | This paper extends the existing line of research of the dynamics of multiplicative weights update and similar algorithms for games. It shows that for zero-sum two-player games and for population games satisfying certain conditions, that the entropy increases linearly as long as the strategies are far from the distribu... | SP:23a6cf043248b37fc8b792217c58a97697d56290 |
The Evolution of Uncertainty of Learning in Games | 1 INTRODUCTION . A primary goal of ML research is to understand the behaviors of learning algorithms in various settings . One standard approach is from each initial condition , we determine whether a learning algorithm converges to a local optimum or stable state . Yet , in the context of online learning in games , an... | The paper studies the evolution of uncertainty in multi-agent game dynamics. More specifically, it studies how the probability distribution over the players' cumulative payoffs evolves as players use typical online learning algorithms to play the game. The game uncertainty is quantified by the notion of Differential E... | SP:23a6cf043248b37fc8b792217c58a97697d56290 |
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions | 1 Introduction . With the explosive amount of data from single-cell genomics studies , one remaining major challenge is the lack of ability to understand cell transition on the individual level . Conventional methods for analyzing single-cell dynamics are mostly based on “ ensemble ” analysis ( Kester & van Oudenaarden... | The authors introduce cell2state, an algorithm that incorporates both genetic barcoding coupled with single-cell sequenced data to model explicit state transitions of cell dynamics over time. Single-cell gene expression profiles are mapped to low-dimensional state vectors that are predictive of cell dynamics. Cell2stat... | SP:a637f040207332aff43cb9d801e4a879ba1dc701 |
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions | 1 Introduction . With the explosive amount of data from single-cell genomics studies , one remaining major challenge is the lack of ability to understand cell transition on the individual level . Conventional methods for analyzing single-cell dynamics are mostly based on “ ensemble ” analysis ( Kester & van Oudenaarden... | Authors proposed cell2state that could embed barcoded scRNA-seq trajectories into low-dimensional representation. Authors provided theoretic analysis of the embedding learnt by cell2state and demonstrated that the learnt embedding was almost lossless. Authors applied this embedding framework on one barcoded scRNA-seq ... | SP:a637f040207332aff43cb9d801e4a879ba1dc701 |
Text-Driven Image Manipulation via Semantic-Aware Knowledge Transfer | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have revolutionized a variety of fields due to its powerful ability to generate realistic and meaningful outputs . Recent works ( Jahanian et al. , 2019 ; He et al. , 2019 ; Goetschalckx et al. , 2019 ) have shown that deep generativ... | The authors proposed a directional latent mapping network for facial attribute editing via text inputs. The directional latent mapping network could correctly edit relevant attributes while preserving irrelevant attributes via training with the semantic direction consistency (SDC) loss. This paved the way to a novel se... | SP:caea798fb6dcc5623f6516a64c2ea94deac2ae02 |
Text-Driven Image Manipulation via Semantic-Aware Knowledge Transfer | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have revolutionized a variety of fields due to its powerful ability to generate realistic and meaningful outputs . Recent works ( Jahanian et al. , 2019 ; He et al. , 2019 ; Goetschalckx et al. , 2019 ) have shown that deep generativ... | This paper proposes a new loss function for unsupervised facial attribute editing and transfer. Specifically, a latent mapping network is trained by optimizing the similarity/distance between the generated and desired image in the CLIP latent space. Experiments show some comparisons and ablations for the "smile" attrib... | SP:caea798fb6dcc5623f6516a64c2ea94deac2ae02 |
Divergence-aware Federated Self-Supervised Learning | 1 INTRODUCTION . Self-supervised learning ( SSL ) has attracted extensive research interest for learning representations without relying on expensive data labels . In computer vision , the common practice is to design proxy tasks to facilitate visual representation learning from unlabeled images ( Doersch et al. , 2015... | This paper investigates a generic federated SSL recipe that applies FedAvg to a range of existing SSL works including SimCLR, MoCo, BYOL and SimSiam. Each of these SSL blocks comprises two encoding networks, including an online net and a target net. The two nets were trained via optimizing a similarity loss such that t... | SP:3bf64bcc780380921a57f996019861e87d24884e |
Divergence-aware Federated Self-Supervised Learning | 1 INTRODUCTION . Self-supervised learning ( SSL ) has attracted extensive research interest for learning representations without relying on expensive data labels . In computer vision , the common practice is to design proxy tasks to facilitate visual representation learning from unlabeled images ( Doersch et al. , 2015... | The authors propose a new approach called Federated Divergence-aware Exponential moving Average update (FedEMA) to avoid the IID assumption. FedEMA is built onto of FedSSL which is a framework for self-supervised learning in a Federated Learning context. The authors proposes a new approach to fuse the local and global ... | SP:3bf64bcc780380921a57f996019861e87d24884e |
Foreground-attention in neural decoding: Guiding Loop-Enc-Dec to reconstruct visual stimulus images from fMRI | 1 INTRODUCTION . In recent years , reconstructing visual stimulus images from fMRI has gradually gained attention , which provides the possibility of ” mind reading ” in the future ( Fig . 1 ) . Existing work has shown that there is a certain mapping relationship between visual stimuli and brain activity ( Poldrack & F... | The paper proposed a Loop-Enc-Dec framework to perform an image reconstruction in a neural decoding task. The solution is based on an end-to-end encoder-decoder model under the guidance of Foreground-attention to enhance the perceptual quality of reconstructed images. The experimental results show visible improvements ... | SP:f39d50648208f976167aeb0aac498effa4bd0a18 |
Foreground-attention in neural decoding: Guiding Loop-Enc-Dec to reconstruct visual stimulus images from fMRI | 1 INTRODUCTION . In recent years , reconstructing visual stimulus images from fMRI has gradually gained attention , which provides the possibility of ” mind reading ” in the future ( Fig . 1 ) . Existing work has shown that there is a certain mapping relationship between visual stimuli and brain activity ( Poldrack & F... | The authors proposed a model to decoding the fMRI signal from the human visual cortex by introducing the Foreground-attention. They also proposed a enc-dec training strategy called Loop-Enc-Dec, which is guided by the F-attention, to successfully reconstruct the visual images from the fMRI data. A higher score based on... | SP:f39d50648208f976167aeb0aac498effa4bd0a18 |
Certified Robustness for Free in Differentially Private Federated Learning | 1 INTRODUCTION . Federated Learning ( FL ) , which aims to jointly train a global model with distributed local data , has been widely applied in different applications , such as finance ( Yang et al. , 2019b ) , medical analysis ( Brisimi et al. , 2018 ) , and user behavior prediction ( Hard et al. , 2018 ; Yang et al.... | ## Update after rebuttal and discussions I thank the authors for taking the time to discuss the issues pointed out in the reviews at length. Unfortunately, I am still not convinced that the paper is ready for publication. My main concerns: 1) There are now experiments in the updated paper claimed to be DP which are n... | SP:8a8ee9de77204eab83867e7170e01e24f0e2d81e |
Certified Robustness for Free in Differentially Private Federated Learning | 1 INTRODUCTION . Federated Learning ( FL ) , which aims to jointly train a global model with distributed local data , has been widely applied in different applications , such as finance ( Yang et al. , 2019b ) , medical analysis ( Brisimi et al. , 2018 ) , and user behavior prediction ( Hard et al. , 2018 ; Yang et al.... | This paper studies differentially private federated learning and its intrinsic robustness against data poisoning attacks. Theoretically, the authors build two definitions for certified robustness against data poisoning attacks, draw the connection with user-level and instance-level differential privacy. The key proof i... | SP:8a8ee9de77204eab83867e7170e01e24f0e2d81e |
Simpler Calibration for Survival Analysis | 1 INTRODUCTION . Survival analysis , also known as time-to-event analysis , is the problem to predict the time of the occurrence of an event . In healthcare applications , the event typically corresponds to a death or the onset of disease in a patient . The time between a well-defined starting point and the occurrence ... | This paper first defines a new definition of calibration which, in contrast to the one used in prior work, is confined to the maximum observed time in the data. They then propose a KM regularizer for making sure their survival curves are calibrated: they are closer to KM curves. They also claim that their regularizatio... | SP:5165c93ec3eefa99a32d00be8d59dd6894bb1d87 |
Simpler Calibration for Survival Analysis | 1 INTRODUCTION . Survival analysis , also known as time-to-event analysis , is the problem to predict the time of the occurrence of an event . In healthcare applications , the event typically corresponds to a death or the onset of disease in a patient . The time between a well-defined starting point and the occurrence ... | In this work, the authors propose a novel approach for learning calibrated predictions for survival analysis and similar tasks. The intuition of the approach is that the average probability that a prediction has a value less than or equal to $t$ should approximately equal the number of observations with value less than... | SP:5165c93ec3eefa99a32d00be8d59dd6894bb1d87 |
Generating High-Fidelity Privacy-Conscious Synthetic Patient Data for Causal Effect Estimation with Multiple Treatments | 1 INTRODUCTION . In health care , studying the causal effect of treatments on patients is critical to advance personalized medicine . Observing an association between a drug ( exposure or treatment ) and subsequent adverse or beneficial event ( outcome ) is not enough to claim the treatment is indeed the cause of the o... | The authors propose a method for using real-world patient data to generate (semi-)synthetic privacy-preserving data on which to evaluate methods for causal effect estimation. For this purpose, they adapt the ADS-GAN (Anonymization through Data Synthesis using Generative Adversarial Networks) model introduced by Yoon et... | SP:355d95d502cd5d5de8ce41d9792253ee06454986 |
Generating High-Fidelity Privacy-Conscious Synthetic Patient Data for Causal Effect Estimation with Multiple Treatments | 1 INTRODUCTION . In health care , studying the causal effect of treatments on patients is critical to advance personalized medicine . Observing an association between a drug ( exposure or treatment ) and subsequent adverse or beneficial event ( outcome ) is not enough to claim the treatment is indeed the cause of the o... | The paper studies the problem of generating synthetic patient data for the evaluation of causal inference models. The generated patient data is expected to highly mimic the distribution of the original dataset while also taking patient privacy into consideration. Experiments are conducted on the synthetic dataset with ... | SP:355d95d502cd5d5de8ce41d9792253ee06454986 |
Adaptive Activation-based Structured Pruning | 1 INTRODUCTION . Deep neural networks ( DNNs ) have substantial compute and memory requirements . As deep learning becomes pervasive and moves towards edge devices , DNN deployment becomes harder because of the mistmatch between resource-hungry DNNs and resource-constrained edge devices . DNN pruning is a promising app... | This paper proposes iterative structured pruning methods using activation-based attention feature maps and an adaptive threshold selection strategy. Inspired by attention transfer, Activation-based attention feature maps are constructed as the important evaluation of filters in each layer. Adaptive threshold selection ... | SP:075aa882a64acdb6d7c9486c235ef657b7afb104 |
Adaptive Activation-based Structured Pruning | 1 INTRODUCTION . Deep neural networks ( DNNs ) have substantial compute and memory requirements . As deep learning becomes pervasive and moves towards edge devices , DNN deployment becomes harder because of the mistmatch between resource-hungry DNNs and resource-constrained edge devices . DNN pruning is a promising app... | This work proposes a technique for iterative structured pruning, without necessarily requiring too much manual human intervention. There are two parts to this paper that are important: 1. It is argued that we should prune channels based on the activation maps generated, rather than focusing on the weights of the chann... | SP:075aa882a64acdb6d7c9486c235ef657b7afb104 |
EViT: Expediting Vision Transformers via Token Reorganizations | 1 INTRODUCTION . Computer vision research has evolved into Transformers since ViTs ( Dosovitskiy et al. , 2021 ) . Equipped with global self-attention , ViTs have shown impressive capability upon local convolution ( i.e. , CNNs ) on prevalent visual recognition scenarios , including image classification ( Dosovitskiy e... | This paper aims to expediting vision transformers by reducing the number of tokens. The main contribution is the attentive token identification, which is based on calculating the attentiveness of the class token with respect to each image token. Experiments on DeiT and LV-ViT show that the proposed approach is able to... | SP:f2f42c4a7163bf5a94e00d0d0ab05c8ea5e44727 |
EViT: Expediting Vision Transformers via Token Reorganizations | 1 INTRODUCTION . Computer vision research has evolved into Transformers since ViTs ( Dosovitskiy et al. , 2021 ) . Equipped with global self-attention , ViTs have shown impressive capability upon local convolution ( i.e. , CNNs ) on prevalent visual recognition scenarios , including image classification ( Dosovitskiy e... | In this paper, an EVIT method is proposed for vision transformer speedup. It reduces image tokens based on the token attentiveness, which is measured by the class token. The inattentive tokens are reorganized as one to support attentive tokens. Experiments have shown on the benchmarks for visual recognition. | SP:f2f42c4a7163bf5a94e00d0d0ab05c8ea5e44727 |
A Study of Face Obfuscation in ImageNet | 1 INTRODUCTION . Visual data is being generated at an unprecedented scale . People share billions of photos daily on social media ( Meeker , 2014 ) . There is one security camera for every 4 people in China and the United States ( Lin & Purnell , 2019 ) . Even your home can be watched by smart devices taking photos ( B... | This paper presents an empirical study on the effect of face obfuscation in the ImageNet dataset. The main conclusion is that face obfuscation does not decrease the utility of the dataset. Specifically, the authors showed that various networks trained on the obfuscated dataset only experienced small accuracy drop on th... | SP:4df661cd71eb3a7947d890ff84e25f48b6b38012 |
A Study of Face Obfuscation in ImageNet | 1 INTRODUCTION . Visual data is being generated at an unprecedented scale . People share billions of photos daily on social media ( Meeker , 2014 ) . There is one security camera for every 4 people in China and the United States ( Lin & Purnell , 2019 ) . Even your home can be watched by smart devices taking photos ( B... | The main concern addressed in this paper is the privacy problem that may result from images in ImageNet databases containing unexpected faces. The authors propose a two-step face filtering method. First, the authors use a detector called Amazon Rekognition to detect the ImageNet database. Then, the authors further opti... | SP:4df661cd71eb3a7947d890ff84e25f48b6b38012 |
Privacy Protected Multi-Domain Collaborative Learning | 1 INTRODUCTION . Unsupervised domain adaptation ( UDA ) ( Tang et al. , 2020 ; Jiang et al. , 2020 ; Zhang et al. , 2020 ) attempts to transfer knowledge from well-labeled source domains to annotate unlabeled target samples , which have significant domain discrepancy with source domains due to the various data collecti... | This paper studies the problem of privacy-protected multi-domain collaborative filtering, in which a "win-win" deal for source and target domains can be achieved. The proposed framework, MDFNet, contains multiple local clients and one global server. In each client, the encoder achieves feature separation, and the decod... | SP:bb43cfb5f54986cf5d310aa141514739712c2fd4 |
Privacy Protected Multi-Domain Collaborative Learning | 1 INTRODUCTION . Unsupervised domain adaptation ( UDA ) ( Tang et al. , 2020 ; Jiang et al. , 2020 ; Zhang et al. , 2020 ) attempts to transfer knowledge from well-labeled source domains to annotate unlabeled target samples , which have significant domain discrepancy with source domains due to the various data collecti... | This paper proposes a Mask-Driven Federated Network (MDFNet) to reach a “win-win” deal for multiple domains with data protected to solve the Privacy Protected Multi-Domain Collaborative Learning (P2MDCL) problem. Specifically, each domain is armed with an individual local model via a mask disentangled mechanism to lear... | SP:bb43cfb5f54986cf5d310aa141514739712c2fd4 |
Convergent Boosted Smoothing for Modeling GraphData with Tabular Node Features | 1 INTRODUCTION . Tabular data consists of observations stored as rows of a table , where multiple numeric/categorical features are recorded for each observation , one per column . Models for tabular data must learn to output accurate predictions solely from ( potentially high-dimensional or sparse ) sets of heterogeneo... | The authors propose a new method for integrating graph-based models with boosting. This is done using the typical method involving residuals and weak-learners, but adding a step where information is propagated in the graph. The approach is also simple, as no GNNs or other auxiliary models are required. It is also shown... | SP:a69b08508dcfffcd0d1f64454e90d3ea2337cb5e |
Convergent Boosted Smoothing for Modeling GraphData with Tabular Node Features | 1 INTRODUCTION . Tabular data consists of observations stored as rows of a table , where multiple numeric/categorical features are recorded for each observation , one per column . Models for tabular data must learn to output accurate predictions solely from ( potentially high-dimensional or sparse ) sets of heterogeneo... | In this paper, the authors present a new approach to combine the boosted decision tree classifiers with a graph propagation model, which is important in handling table input data. The approach casts the graph propagation as an optimization problem, where the input node features are generated by boosted decision trees. ... | SP:a69b08508dcfffcd0d1f64454e90d3ea2337cb5e |
Softmax Gradient Tampering: Decoupling the Backward Pass for Improved Fitting | 1 INTRODUCTION . Smooth gradient flow is the key to successful convergence of deep neural networks . Batch Normalizing Ioffe & Szegedy ( 2015 ) , Weight Standardization Qiao et al . ( 2019 ) , and Group Normalization Wu & He ( 2018 ) are all types of normalization techniques that smooth the gradient landscape in the ba... | In this paper, the authors propose a technique called Softmax Gradient Tampering, which transforms the predicted output class probabilities to improve training performance of neural networks. The authors show that the proposed technique results in a smoother output probability distribution for lower values of the hyper... | SP:90cd795a78c403b27c768be54597bd04cc0fbaa3 |
Softmax Gradient Tampering: Decoupling the Backward Pass for Improved Fitting | 1 INTRODUCTION . Smooth gradient flow is the key to successful convergence of deep neural networks . Batch Normalizing Ioffe & Szegedy ( 2015 ) , Weight Standardization Qiao et al . ( 2019 ) , and Group Normalization Wu & He ( 2018 ) are all types of normalization techniques that smooth the gradient landscape in the ba... | This paper proposes the softmax gradient tampering to modify the gradients in the backward pass to enhance the accuracy. The predicted probability value is transformed using a power-based probability transformation, and the gradient profile is more smooth. The experimental results show the slight accuracy increase. | SP:90cd795a78c403b27c768be54597bd04cc0fbaa3 |
Calibration Regularized Training of Deep Neural Networks using Kernel Density Estimation | 1 INTRODUCTION . Deep neural networks have shown tremendous success in classification tasks , being regularly the best performing models in terms of accuracy . However , they are also known to make overconfident predictions ( Guo et al. , 2017 ) , which is particularly problematic in safety-critical applications such a... | The paper proposes a new approach for calibrating neural network outputs. The idea is to train the neural network with a regularized loss function that is a linear combination of prediction and calibration errors. The calibration error is measured as the Lp norm of the difference between predicted class probabilities a... | SP:59bd93781598c2b893c92d41b3cad91b5f719e57 |
Calibration Regularized Training of Deep Neural Networks using Kernel Density Estimation | 1 INTRODUCTION . Deep neural networks have shown tremendous success in classification tasks , being regularly the best performing models in terms of accuracy . However , they are also known to make overconfident predictions ( Guo et al. , 2017 ) , which is particularly problematic in safety-critical applications such a... | The paper proposes a regularization term to augment loss functions, where the regularization term effectively minimizes the calibration error of the model. The term itself is a kernel density estimator over the K-simplex space (hence Dirichlet kernel is the natural choice). The authors claim the estimator is consisten... | SP:59bd93781598c2b893c92d41b3cad91b5f719e57 |
A global convergence theory for deep ReLU implicit networks via over-parameterization | 1 INTRODUCTION . 1 ) Background and Motivation : In the last decade , implicit deep learning ( El Ghaoui et al. , 2019 ) have attracted more and more attention . Its popularity is mainly because it generalizes the recursive rules of many widely used neural network architectures . A line of recent works ( Bai et al. , 2... | In this paper, the authors theoretically analyze the convergence of gradient descent for an implicit neural network with infinite layers with ReLU activation. The authors show the unique fixed point of the infinite-layered mapping when the weight matrix $\boldsymbol{A}$ has a properly bounded spectral norm. Using im... | SP:c8330db4743ced525c85be1261a538cfc9ad4e35 |
A global convergence theory for deep ReLU implicit networks via over-parameterization | 1 INTRODUCTION . 1 ) Background and Motivation : In the last decade , implicit deep learning ( El Ghaoui et al. , 2019 ) have attracted more and more attention . Its popularity is mainly because it generalizes the recursive rules of many widely used neural network architectures . A line of recent works ( Bai et al. , 2... | The paper presents a proof of exponential convergence to global optimality in the over-parametrization settings for an implicit model with scaled weights parameters. Although existing work has established similar proofs for feedforward explicit neural networks, such methods don't work with non-linearly activated implic... | SP:c8330db4743ced525c85be1261a538cfc9ad4e35 |
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack | The AutoAttack ( AA ) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available . However , the high computational cost ( e.g. , 100 times more than that of the project gradient descent attack ) makes AA infeasible for practitioners with limited computa... | The paper proposed a strong adversarial attack, i.e., an attack that can generate strong adversarial examples and thus can better evaluate the adversarial robustness of given deep learning models. Compared with the SOTA attack, the proposed attack is much faster and thus easier to be applied in practice. The idea is no... | SP:291bb805fd27e09408f36ac44529a4e399838004 |
Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack | The AutoAttack ( AA ) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available . However , the high computational cost ( e.g. , 100 times more than that of the project gradient descent attack ) makes AA infeasible for practitioners with limited computa... | This paper proposes a minimum-margin (MM) attack to evaluate defenses. The authors report detailed results on the effects of different loss functions. Experiments are done on CIFAR-10/100 and SVHN, against the adversarially trained models. | SP:291bb805fd27e09408f36ac44529a4e399838004 |
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 studies the traffic forecasting problem and proposes to conduct prediction by pattern matching. Authors first extract key patterns from the historical data in an offline manner and then fetch the patterns for each time series with a distance function (e.g., cosine similarity). Then, the patterns of different... | SP:4a83a8ba8190703c509ecc17fbdc70e82e67d6c8 |
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