MCDAL Maximum Classifier Discrepancy for Active Learning

Recent state-of-the-art active learning methods have mostly leveragedGenerative Adversarial Networks (GAN) for sample acquisition . However, GAN isusually known to suffer from instability and sensitivity to hyper-parameters . In contrast to these methods, we propose in this paper a novel active learningframework that we call Maximum Classifier Discrepancy for Active Learning(MCDAL) which takes the prediction discrepancies between multiple classifiers .…

LocalGLMnet interpretable deep learning for tabular data

Deep learning models have gained great popularity in statistical modeling . Theadvantage of deep learning models is that their solutions are difficult tointerpret and explain . We propose a new network architecture that sharessimilar features as generalized linear models, but provides superior predictivepower benefiting from the art of representation learning .…

Lower Bounds for Symmetric Circuits for the Determinant

Dawar and Wilsenach (ICALP 2020) show an exponential separation between the sizes of symmetric arithmeticcircuits for computing the determinant and the permanent . The symmetryrestriction is that the circuits which take a matrix input are unchanged by a permutation applied simultaneously to the rows and columns of the matrix .…

Lower Bounds for Symmetric Circuits for the Determinant

Dawar and Wilsenach (ICALP 2020) show an exponential separation between the sizes of symmetric arithmeticcircuits for computing the determinant and the permanent . The symmetryrestriction is that the circuits which take a matrix input are unchanged by a permutation applied simultaneously to the rows and columns of the matrix .…

Learning Quadruped Locomotion Policies with Reward Machines

Legged robots have been shown to be effective in navigating unstructured environments . There has been much success in learning locomotionpolicies for quadruped robots, but there is little research on how to incorporatehuman knowledge to facilitate this learning process . In this paper, wedemonstrate that human knowledge in the form of LTL formulas can be applied to quadruped locomotion learning within a Reward Machine framework .…

User Perception of Privacy with Ubiquitous Devices

Privacy is important for all individuals in everyday life . With emergingtechnologies, smartphones with AR, various social networking applications and modes of surveillance, they tend to intrudeprivacy . This study aimed to explore and discover various concerns related toperception of privacy in this era of ubiquitous technologies .…

Data driven deep density estimation

Density estimation plays a crucial role in many data analysis tasks . It is used in tasks as diverse as analyzing population data, spatiallocations in 2D sensor readings, or reconstructing scenes from 3D scans . In this paper, we introduce a learned, data-driven deep density estimation (DDE)to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size .…

When a crisis strikes Emotion analysis and detection during COVID 19

Understanding emotions that people express during large-scale crises helps inform policy makers and first responders about theemotional states of the population . We present CovidEmo, ~1K tweets labeled withemotions. We examine how well large pre-trained language models generalizeacross domains and crises in the task of perceived emotion prediction .…

Wavelet Design in a Learning Framework

Wavelets have proven to be highly successful in several signal and image processing applications . We aim atdesigning data-independent wavelets by training filterbank autoencoders, which .precludes the need for customized datasets . We show that anear-zero training loss implies that the learnt filters satisfy the perfect .reconstruction…

Constellation Learning relational abstractions over objects for compositional imagination

Constellation is anetwork that learns relational abstractions of static visual scenes, and generalises these abstractions over sensory particularities . We further show that thisbasis, along with language association, provides a means to imagine sensorycontent in new ways . This work is a first step in the explicit representation of visual relationships and using them for complex cognitive procedures .…

Formalizing Galois Theory

We describe a project to formalize Galois theory using the Lean theoremprover . We discuss some of the challenges we faced and the decisions we made in the course of this project . The maintheorems we formalized are the primitive element theorem, the fundamentaltheorem of Galois Theory, and the equivalence of several characterizations offinite degree Galois extensions .…

Developing efficient transfer learning strategies for robust scene recognition in mobile robotics using pre trained convolutional neural networks

We present four different robust transfer learning strategies for robust mobile scene recognition . Fine-Tuning in combination withextensive data augmentation improves accuracy and robustness in mobile robot place recognition . We achieved state-of-the-art results using variousbaseline convolutional neural networks and showed the robustness againstlighting and viewpoint changes in challenging mobile robot places recognition .…

3D Radar Velocity Maps for Uncertain Dynamic Environments

Future urban transportation concepts include a mixture of ground and air vehicles with varying degrees of autonomy in a congested environment . Safe and efficient transportation requires reasoning about the 3Dflow of traffic and properly modeling uncertainty . This paper explores a Bayesian approach that captures our uncertainty in the map given training data .…

A Logic of Expertise

In this paper we introduce a simple modal logic framework to reason about theexpertise of an information source . In the framework, a source is an expert on a proposition $p$ if they are able to correctly determine the truth value of$p$ in any possible world .…

An Adaptive State Aggregation Algorithm for Markov Decision Processes

Value iteration is a well-known method of solving Markov Decision Processes . However, the computational cost of value iteration quickly becomesfeasible as the size of the state space increases . In this paper, we propose an intuitive algorithm for solving MDPsthat reduces the cost of updates by dynamically grouping together states with similar cost-to-go values .…

Hash Based Tree Similarity and Simplification in Genetic Programming for Symbolic Regression

We introduce a runtime-efficient tree hashing algorithm for the identification of isomorphic subtrees . We propose a simple diversity-preservationmechanism with promising results on a collection of symbolic regressionbenchmark problems . The algorithm has two important applications: fast calculation ofpopulation diversity and algebraic simplification of symbolic expression trees .…

Bio inspired Rhythmic Locomotion in a Six Legged Robot

Developing a framework for the locomotion of a hexapod is a complex task that has extensive hardware and computational requirements . Our locomotion model draws inspiration from the structure of acockroach, with its fairly simple central nervous system, and results in our model being computationally inexpensive with simpler control mechanisms .…

An Improved Algorithm of Robot Path Planning in Complex Environment Based on Double DQN

Deep Q Network (DQN) has several limitations when applied in planning a path in an environment with a number of dilemmas according to our experiment . In this context, this paper proposes animproved Double DQN to solve the problem by reference to A* andRapidly-Exploring Random Tree (RRT) In order to achieve the rich experimentsin experience replay, the initialization of robot in each training round is defined based on RRT strategy .…

Adaptively Weighted Top N Recommendation for Organ Matching

Organ matching decision is the most critical decision to assign limited viable organs to the most suitable patients . Currently, organ matching decisions were only made by matching scores calculated viascoring models . AWTR improves performance of the current scoring models by using limited actual matching performance in historical data set as well as thecollected covariates from organ donors and patients .…

AD GAN End to end Unsupervised Nuclei Segmentation with Aligned Disentangling Training

Aligned Disentangling Generative AdversarialNetwork (AD-GAN) introduces representationdisentanglement to separate content representation from style representation . With this framework, spatial structure can be preserved explicitly, enabling asignificant reduction of macro-level lossy transformation . AD-GAN leads to significant improvement over the current best unsupervised methods by an average 17.8% relatively (w.r.t.…

Human Pose Regression with Residual Log likelihood Estimation

Heatmap-based methods dominate in the field of human pose estimation by modelling the output distribution through likelihood heatmaps . Residual Log-likelihood Estimation(RLE) is effective, efficient and flexible . Compared to the conventional regression paradigm, regression with RLE bring 12.4 mAPimprovement on MSCOCO without any test-time overhead .…

Cardiac CT segmentation based on distance regularized level set

A paper uses distanceregularized level set (DRL SE) to explore the segmentation effect of epicardiumand endocardium . Five CT images are used to verify the proposedmethod, and image quality evaluation indexes such as dice score and Hausdorffdistance are used . The results showed that the researchers could separate the inner and outer membrane very well (endocardiumdice = 0.9253, Hausorfff = 7.8740) and epicocardium Hausdice= 0.9687 .…

Bias Loss for Mobile Neural Networks

Compact convolutional neural networks (CNNs) have witnessed exceptionalimprovements in performance in recent years . However, they still fail toprovide the same predictive power as CNNs with a large number of parameters . Diverse features present in activation maps indicate presence of unique descriptors necessary to distinguish between objects of differentclasses .…

VisDA 2021 Competition Universal Domain Adaptation to Improve Performance on Out of Distribution Data

Visual DomainAdaptation (VisDA) 2021 competition tests models’ ability to adapt to novel test distributions and handle distributional shift . Ourchallenge draws on large-scale publicly available datasets but constructs the evaluation across domains . Performance will be measured using a rigorous protocol,comparing to state-of-the-art domain adaptation methods with the help ofestablished metrics .…

Resource Efficient Mountainous Skyline Extraction using Shallow Learning

Skyline plays a pivotal role in mountainous visual geo-localization and localization/navigation of planetary rovers/UAVs and virtual/augmented realityapplications . We present a novel mountainous skyline detection approach wherewe adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions .…

Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds

Semantic segmentation on 3D point clouds is an important task for 3D sceneunderstanding . We train a semantic point cloud segmentation network with only asmall portion of points being labeled . We argue that we can better utilize thelimited supervision information as we densely propagate the supervision signalfrom the labeled points to other points within and across the input samples .…

MCDAL Maximum Classifier Discrepancy for Active Learning

Recent state-of-the-art active learning methods have mostly leveragedGenerative Adversarial Networks (GAN) for sample acquisition . However, GAN isusually known to suffer from instability and sensitivity to hyper-parameters . In contrast to these methods, we propose in this paper a novel active learningframework that we call Maximum Classifier Discrepancy for Active Learning(MCDAL) which takes the prediction discrepancies between multiple classifiers .…

Robust Adaptive Submodular Maximization

Most of existing studies on adaptive submodular optimization focus on theaverage-case, i.e., their objective is to find a policy that maximizes theexpected utility over a known distribution of realizations . We introduce a new class ofstochastic functions, called \emph{worst-case submodular function .…