Self Supervised Learning by Estimating Twin Class Distributions

TWIST is a novel self-supervised representation learning method by classifying large-scale unlabeled datasets in an end-to-end way . We employ asiamese network terminated by a softmax operation to produce twin class distributions of two augmented images . Without supervision, we enforce theclass distributions of different augmentations to be consistent .…

Automatic Generation of Grover Quantum Oracles for Arbitrary Data Structures

Quantum oracles that are frequently masked asblack boxes play an important role in Grover’s algorithm . The automaticgeneration of the corresponding circuits for a Grover quantum oracle is deeplylinked to the synthesis of reversible quantum logic . Despite numerous advances in the field, quantum computing still remains a challenge till today in terms ofsynthesizing efficient and scalable circuits for complex boolean functions .…

Region Semantically Aligned Network for Zero Shot Learning

Zero-shot learning (ZSL) aims to recognize unseen classes based on the knowledge of seen classes . Previous methods focused on learning directembeddings from global features to the semantic space in hope of knowledgetransfer from seen classes to unseen classes . Instead of using globalfeatures which are obtained by an average pooling layer after an image encoder, we directly utilize the output of the image .…

Compressibility of Distributed Document Representations

We propose CoRe, a straightforward, representation learner-agnostic framework suitable for representation compression . The CoRe’s performance was studied on a collection of 17 real-life corpora from biomedical,news, social media, and literary domains . We explored the behavior whenconsidering contextual and non-contextual document representations, differentcompression levels, and 9 different compression algorithms .…

Capacity of Group invariant Linear Readouts from Equivariant Representations How Many Objects can be Linearly Classified Under All Possible Views

Equivariance has emerged as a desirable property of representations of objects subject to identity-preserving transformations that constitute a group . However, the expressivity of arepresentation constrained by group equivariance is still not fully understood . We provide a generalization of Cover’s Function CountingTheorem that quantifies the number of linearly separable and group-invariantbinary dichotomies that can be assigned to equivariant representations .…

On the ESL algorithm for solving energy games

We propose a variant of an algorithm introduced by Schewe and also studied by Luttenberger for solving parity or mean-payoff games . We present it over energy games and call our variant the ESL algorithm . We find that using potential reductions as introduced by Gurvich, Karzanov andKhachiyan allows for a simple and elegant presentation of the algorithm .…

EPROACH A Population Vaccination Game for Strategic Information Design to Enable Responsible COVID Reopening

A population of players decides whether to vaccinate or not based on the public and private information they receive . The equilibrium vaccination decisions depend on the information received by the agents . The insightsobtained from our framework include the appropriate vaccination coveragethreshold for safe-reopening and information-based methods to incentivize individual vaccination decisions .…

Comparative Opinion Summarization via Collaborative Decoding

Opinion summarization focuses on generating summaries that reflect popularopinions of multiple reviews for a single entity . We propose a task to generate two contrastive summaries and onecommon summary from two given sets of reviews from different entities . Wedeveloped a comparative summarization framework CoCoSum, which consists of twofew-shot summarization models that are jointly used to generate contrastive andcommon summaries .…

Graph Condensation for Graph Neural Networks

We aim to condense large, original graph into a small, synthetic and highly-informative graph . We are able to approximate the original test accuracy by 95.3% on Reddit, 99.8% on Flickr and 99.0% on Citeseer . Extensive experiments have demonstrated the effectiveness of the proposed framework in condensing different graph datasets into informativesmaller graphs .…

Order Constraints in Optimal Transport

Recent works have aimed toimprove optimal transport plans through the introduction of various forms of structure . We introduce novel order constraints into the optimal transportformulation to allow for structure . While there will are now quadratically many constraints as before, we prove a .roximatesolution…

Offline Reinforcement Learning with Soft Behavior Regularization

Most prior approaches to offline reinforcement learning (RL) utilize behavior regularization, typically augmenting existing off-policyactor critic algorithms with a penalty measuring divergence between the policy and the offline data . We propose a practical way to compute the density ratio and demonstrate its equivalence to a state-dependentbehavior regularization .…

Sub word Level Lip Reading With Visual Attention

The goal of this paper is to learn strong lip reading models that can recognise speech in silent videos . We use sub-word units for lip reading for the first time to better model the ambiguities of the task . Our best lip reading model achieves 22.6% word error rate on the LRS2 dataset, a performanceunprecedented for lip-reading models, significantly reducing the performance gap between lip reading and automatic speech recognition .…

MReD A Meta Review Dataset for Controllable Text Generation

Using existing text generation datasets for controllable text summarization, we are facing the problem of not having the domain knowledge and thus the aspects that could be controlled are limited . MReD consists of 7,089 meta-reviews and all its 45k sentences are manually annotated as one of the 9 categories, including abstract, strength, decision, etc.…

SpongeCake A Layered Microflake Surface Appearance Model

In this paper, we propose SpongeCake: a layered BSDF model where each layer is a volumetric scattering medium . We omit any reflecting and refracting interfaces between the layers . Despite theabsence of layer interfaces, we demonstrate that many common material effectscan be achieved with layers of SGGX microflake and other volumes with appropriate parameters .…

sMGC A Complex Valued Graph Convolutional Network via Magnetic Laplacian for Directed Graphs

Recent advancements in Graph Neural Networks have led to state-of-the-art performance on representation learning of graphs for node classification . However, the majority of existing works process directed graphs bysymmetrization, which may cause loss of directional information . In this paper, we propose the magnetic Laplacian that preserves edge directionality by encoding it into complex phase as a deformation of the combinatorial LaPlacian .…

Bugs in our Pockets The Risks of Client Side Scanning

Some in industry and government now advocate a new technology to access targeted data: client-side scanning . CSS would enable on-device analysis of data in the clear . CSS by its nature createsserious security and privacy risks for all society while it can provide assistance for law enforcement is at best problematic, authors say .…

Learning Temporal 3D Human Pose Estimation with Pseudo Labels

We present a simple, yet effective, approach for self-supervised 3D humanpose estimation . During training, we rely ontriangulating 2D body pose estimates of a multiple-view camera system . Atemporal convolutional neural network is trained with the generated 3Dground-truth and the geometric multi-view consistency loss, imposinggeometrical constraints on the predicted 3D body skeleton .…

Socially assistive robots deployment in healthcare settings a global perspective

Social robots are finding their place in societyis for healthcare-related applications . Yet, very little research has mapped the deployment of socially assistive robots in real settings . Using adocumentary research method, we were able to trace back 279 experiences of SARsdeployments in hospitals, elderly care centers, occupational health centers, private homes, and educational institutions worldwide .…

A Dual Attention Neural Network for Pun Location and Using Pun Gloss Pairs for Interpretation

Pun location is to identify the punning word (usually a word or phrase that makes the text ambiguous) in a given short text . Pun interpretation is to find out two different meanings of the word . DANN (Dual-Attentive Neural Network) is proposed for pun location, effectively integrates word senses and pronunciation with context information to address two kinds of pun at the same time .…

Domain Adaptation on Semantic Segmentation with Separate Affine Transformation in Batch Normalization

In recent years, unsupervised domain adaptation (UDA) for semanticsegmentation has brought many researchers’attention . The proposed SEAT is simple, easily implemented and easy to integrate into existing adversarial learning based UDA methods . We introduce multi level adaptation by adding thelower-level features to the higher-level ones before feeding them to the discriminator, without adding extra discriminator like others.…

Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution

In this paper, we introduce a variational Bayesian algorithm (VBA) for imageblind deconvolution . Our generic framework incorporates smoothness priors on the unknown blur/image and possible affine constraints (e.g., sum to one) on the blur kernel . One of our main contributions is the integration of VBA withina neural network paradigm, following an unrolling methodology .…

The Neural MMO Platform for Massively Multiagent Research

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular gamesystems . We present Neural MMO as free and opensource software with active support, ongoing development, documentation, and training, logging, and visualization tools to help users adapt to the new setting .…

HUMAN4D A Human Centric Multimodal Dataset for Motions and Immersive Media

We introduce HUMAN4D, a large and multimodal 4D dataset that contains avariety of human activities simultaneously captured by a professionalmarker-based MoCap, a volumetric capture and an audio recording system . By capturing 2 female and $2$ male professional actors performing variousfull-body movements and expressions, we provide a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities .…

A more direct and better variant of New Q Newton s method Backtracking for m equations in m variables

In this paper we propose a variant of New Q-Newton’s method Backtracking . The update rule of our method is $x\mapsto x-\gamma (x)w(x)$. Good theoretical guarantees are proven, in particular for systems ofpolynomial equations . In “generic situations”, we will also discuss a way to avoid that the limit of the constructed sequence is a solution of $H(x), but not of $F(x)=0$ The limit is a .…

The Neural MMO Platform for Massively Multiagent Research

Neural MMO is a computationally accessible research platform that combines large agent populations, long time horizons, open-ended tasks, and modular gamesystems . We present Neural MMO as free and opensource software with active support, ongoing development, documentation, and training, logging, and visualization tools to help users adapt to the new setting .…

Conformer Based Self Supervised Learning for Non Speech Audio Tasks

In this paper, we propose a self-supervised audio representationlearning method and apply it to a variety of downstream non-speech audio tasks . We achieve a mean average precision(mAP) score of 0.415, which is a new state-of-the-art on this dataset . Our fine-tuned conformers also surpass ormatch the performance of previous systems pre-trained in a supervised way on several downstream tasks, we say .…

Secure Precoding in MIMO NOMA A Deep Learning Approach

A novel signaling design for secure transmission over two-user multiple-inputmultiple-output non-orthogonal multiple access channel using deep neuralnetworks (DNNs) is proposed . The goal of the DNN is to form the covariancematrix of users’ signals such that the message of each user is transmitted reliably while being confidential from its counterpart .…

Adaptive Differentially Private Empirical Risk Minimization

We propose an adaptive (stochastic) gradient perturbation method for empirical risk minimization . At each iteration, therandom noise added to the gradient is optimally adapted to the stepsize . We prove that the ADP method considerably improves the efficiency guarantee compared to the standard differentially private method inwhich vanilla random noise is added .…

HAVEN Hierarchical Cooperative Multi Agent Reinforcement Learning with Dual Coordination Mechanism

Multi-agent reinforcement learning often suffers from the exponentially larger action space caused by a large number of agents . We propose a novel value decomposition framework HAVEN based on hierarchicalreinforcement learning for the fully cooperative multi-agent problems . Ourmethod is demonstrated to achieve superior results to many baselines onStarCraft II micromanagement tasks and offers an efficient solution tomulti-agent hierarchical reinforcement learning in fully cooperative scenarios .…