Representation Learning for Clustering via Building Consensus

Recent advances in deep clustering and unsupervisedrepresentation learning are based on the idea that different views of an inputimage must be closer in therepresentation space . Consensus Clustering usingUnsupervised Representation Learning (ConCURL) improves the clusteringperformance over state-of-the art methods on four out of five image datasets .…

NeuralLog a Neural Logic Language

The main goal of NeuralLog is to bridge logic programming and deep learning . The main advantages of neural networks are: to allow neural networks to be defined as logic programs; and to be able to handlenumeric attributes and functions .…

Viewport Aware Dynamic 360 Video Segment Categorization

Unlike conventional videos, 360{\deg videos give freedom to users to turntheir heads, watch and interact with content owing to its immersivespherical environment . Although these movements are arbitrary, similarities can be observed between viewport patterns of different users and different videos .…

VersaGNN a Versatile accelerator for Graph neural networks

Graph Neural Network (GNN) is a promising approach for analyzinggraph-structured data . It has achieved state-of-the-art performances in many tasks, such as node classification, graph matching, clustering, and graphgeneration . As GNNs operate on non-Euclidean data, their irregular data accesspatterns cause considerable computational costs and overhead on conventionalarchitectures such as CPU and CPU .…

Bring Your Own Codegen to Deep Learning Compiler

Deep neural networks (DNNs) have been ubiquitously applied in many applications . To achieve highmodel coverage with high performance, each accelerator vendor has to develop afull compiler stack to ingest, optimize, and execute the DNNs . To address these issues, this paper proposes an open source framework that enables users to only concentrate on the development of their own code generation tools by reusing as many as possible components inthe existing deep learning compilers .…

Neural Weighted A Learning Graph Costs and Heuristics with Differentiable Anytime A

We propose Neural Weighted A* a differentiable planner able to produce improved representations of planar maps asgraph costs and heuristics . Training occurs end-to-end on raw images and direct supervision on planning examples . We outperform similar architectures in planning accuracy and efficiency, and can trade offplanning accuracy for efficiency at run-time, using a single, real-valuedparameter .…

Spanners in randomly weighted graphs independent edge lengths

We show that for a large class of graphs with suitabledegree and expansion properties with independent exponential mean one edgelengths, there is w.h.p.~a 1-spanner that uses $\approx \frac12n\log n$ edgesand that this is best possible . In particular, our result applies to the randomgraphs $G_{n,p}$ for $np\gg \log n$, in particular .…

Deep Reinforcement Learning for Adaptive Exploration of Unknown Environments

The proposed approach uses a map segmentation technique to decompose the environment map into smaller, tractable maps . A simple information gain function is computed to determine the best target region to search during eachiteration of the process . DDQN and A2C algorithms are extended with a stack ofLSTM layers and trained to generate optimal policies for the exploration andexploitation, respectively .…

Weak Multi View Supervision for Surface Mapping Estimation

We propose a weakly-supervised multi-view learning approach to learn category-specific surface mapping without dense annotations . We learn theunderlying surface geometry of common categories, such as human faces, cars,and airplanes, given instances from those categories . Our approach leverages information from multiple views of the object to establish additional consistency cycles, thus improving surface mapping understanding .…

COMISR Compression Informed Video Super Resolution

Most video super-resolution methods focus on restoring high-resolution videoframes from low-resolution videos without taking into account compression . Most videos on the web or mobile devices are compressed, and the compression can be severe when the bandwidth is limited . In this paper, we propose a new compression-informed video-super-resolution model to restorehigh-resolution content without introducing artifacts caused by compression .…

ZEN 2 0 Continue Training and Adaption for N gram Enhanced Text Encoders

Pre-trained text encoders have drawn sustaining attention in natural languageprocessing (NLP) They have shown their capability in obtaining promising results indifferent tasks . We propose topre-train n-gram-enhanced Encoders with a large volume of data and advanced techniques for training . We try to extend the encoder to different languages as well as different domains, where it is confirmed that the samarchitecture is applicable to these varying circumstances and new state-of-the-art performance is observed from a long list of NLP tasks across the languages and domains .…

Unsupervised Graph based Topic Modeling from Video Transcriptions

The model improvescoherence by exploiting neural word embeddings through a graph-based clusteringmethod . Unlike typical topic models, this approach works without knowing the true number of topics . Experimental results on the real-life multimodal dataset MuSe-CaR demonstrates that our approach extracts coherent and meaningfultopics, outperforming baseline methods .…

Eigenfactor

The Eigenfactor is a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington . It establishes the importance, influence or impact of a journal based on its location in a network of journals . While journal-basedmetrics have been criticized, it has also been suggested as analternative in the widely used San Francisco Declaration on Research Assessment(DORA) The algorithm is based on Eigenvector centrality, i.e.…

Simplified Klinokinesis using Spiking Neural Networks for Resource Constrained Navigation on the Neuromorphic Processor Loihi

C. elegans shows chemotaxis using klinokinesis where the worm senses the concentration based on a single concentration sensor to compute the concentration gradient to perform foraging through gradient ascent/descenttowards the target concentration . The biomimeticimplementation requires complex neurons with multiple ion channel dynamics aswell as interneurons for control .…

Effects of Quantization on the Multiple Round Secret Key Capacity

We consider the strong secret key (SK) agreement problem for the satellitecommunication setting . Legitimate receivers have access to an authenticated, noiseless, two-way, and public communication link . The noise variances for Alice’s and Bob’s measurement channels are both fixed to a value $Q1$, whereas the noise over Eve’s measurement channel has a unitvariance, so $Q$ represents a channel quality ratio .…

Switching 3 edge colorings of cubic graphs

The chromatic index of a cubic graph is either 3 or 4 . Edge-Kempe switching can be used to transform edge-colorings of cubic graphs . It is further connected to cycle switching of Steiner triple systems, for example, for which an improvement of the classification algorithm is presented .…

Semantic Extractor Paraphraser based Abstractive Summarization

The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models . In this manuscript, we propose an extractor-paraphraser based abstractivesummarization system that exploits semantic overlap as opposed to itspredecessors that focus more on syntactic information overlap .…

Regret Optimal Full Information Control

We consider the infinite-horizon, discrete-time full-information controlproblem . Motivated by learning theory, as a criterion for controller design we focus on regret . In thefull-information setting, there is a unique optimal non-causal controller that dominates all other controllers . The regret-optimal controller is the sum of the classical $H_2$ state-feedback law and a finite-dimensional controller obtainedfrom the Nehari problem .…

Two Stage Facility Location Games with Strategic Clients and Facilities

We consider non-cooperative facility location games where both facilities and clients act strategically and heavily influence each other . This contrasts established game-theoretic facility location models with non-strategic clientsthat simply select the closest opened facility . We focus on a natural client behavior similar to classical loadbalancing: our selfish clients aim for a distribution that minimizes their maximum waiting times for getting serviced, where a facility’s waiting timecorresponds to its total attracted client weight .…

Robustness Enhancement of Object Detection in Advanced Driver Assistance Systems ADAS

A unified system integrating a compact object detector and a surroundingenvironmental condition classifier for enhancing the robustness of objectdetection scheme in advanced driver assistance systems (ADAS) is proposed in this paper . The proposed system includes two main components: (1) a compactone-stage object detector which is expected to be able to perform at acomparable accuracy compared to state-of-the-art object detectors, and (2) an environmental condition detector that helps to send a warning signal to the cloud in case the self-driving car needs human actions due to the significance of the situation .…

Self Supervised Approach for Facial Movement Based Optical Flow

Deep learning-based optical flow techniques do not perform well for non-rigidmovements such as those found in faces . We hypothesize that learning opticalflow on face motion data will improve the quality of predicted flow on faces . The performance of FlowNetS trained on face images surpassed that of other opticalflow CNN architectures, demonstrating its usefulness.…

Intelligent Zero Trust Architecture for 5G 6G Tactical Networks Principles Challenges and the Role of Machine Learning

An intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components . We introduce key ZT principles as real-time Monitoring of thesecurity state of network assets, Evaluating the risk of individual access requests, and Deciding on access authorization using a dynamic trust algorithm,called MED components .…

Data Efficient Reinforcement Learning for Malaria Control

The main challenge faced by policymakers is to learn a policy from scratch by interacting with a complex environment in a few trials . This work introduces apractical, data-efficient policy learning method, named Variance-Bonus MonteCarlo Tree Search~(VB-MCTS) It can copy with very little data andfacilitate learning from scratch in only a few trial times .…