pwc-archive/papers-with-abstracts huggingface dataset:
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition
Authors: Yulin Wang, Yang Yue, Yuanze Lin, Haojun Jiang, Zihang Lai, Victor Kulikov, Nikita Orlov, Humphrey Shi, Gao Huang
Date: 2021-12-28 00:00:00
Proceeding: CVPR 2022 1
Tasks: Computational Efficiency, Diversity, Video Recognition

Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy. As a representative work, the adaptive focus method (AdaFocus) has achieved a favorable trade-off between accuracy and inference speed by dynamically identifying and attending to the informative regions in each video frame. However, AdaFocus requires a complicated three-stage training pipeline (involving reinforcement learning), leading to slow convergence and is unfriendly to practitioners. This work reformulates the training of AdaFocus as a simple one-stage algorithm by introducing a differentiable interpolation-based patch selection operation, enabling efficient end-to-end optimization. We further present an improved training scheme to address the issues introduced by the one-stage formulation, including the lack of supervision, input diversity and training stability. Moreover, a conditional-exit technique is proposed to perform temporal adaptive computation on top of AdaFocus without additional training. Extensive experiments on six benchmark datasets (i.e., ActivityNet, FCVID, Mini-Kinetics, Something-Something V1&V2, and Jester) demonstrate that our model significantly outperforms the original AdaFocus and other competitive baselines, while being considerably more simple and efficient to train. Code is available at https://github.com/LeapLabTHU/AdaFocusV2.
Skin feature point tracking using deep feature encodings
Authors: Jose Ramon Chang, Torbjörn E. M. Nordling
Date: 2021-12-28 00:00:00
Tasks: Heart rate estimation, Image Registration, Point Tracking

Facial feature tracking is a key component of imaging ballistocardiography (BCG) where accurate quantification of the displacement of facial keypoints is needed for good heart rate estimation. Skin feature tracking enables video-based quantification of motor degradation in Parkinson's disease. Traditional computer vision algorithms include Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Lucas-Kanade method (LK). These have long represented the state-of-the-art in efficiency and accuracy but fail when common deformations, like affine local transformations or illumination changes, are present. Over the past five years, deep convolutional neural networks have outperformed traditional methods for most computer vision tasks. We propose a pipeline for feature tracking, that applies a convolutional stacked autoencoder to identify the most similar crop in an image to a reference crop containing the feature of interest. The autoencoder learns to represent image crops into deep feature encodings specific to the object category it is trained on. We train the autoencoder on facial images and validate its ability to track skin features in general using manually labeled face and hand videos. The tracking errors of distinctive skin features (moles) are so small that we cannot exclude that they stem from the manual labelling based on a $\chi^2$-test. With a mean error of 0.6-4.2 pixels, our method outperformed the other methods in all but one scenario. More importantly, our method was the only one to not diverge. We conclude that our method creates better feature descriptors for feature tracking, feature matching, and image registration than the traditional algorithms.
Towards continual task learning in artificial neural networks: current approaches and insights from neuroscience
Authors: David McCaffary
Date: 2021-12-28 00:00:00
Tasks: Continual Learning, Machine Translation

The innate capacity of humans and other animals to learn a diverse, and often interfering, range of knowledge and skills throughout their lifespan is a hallmark of natural intelligence, with obvious evolutionary motivations. In parallel, the ability of artificial neural networks (ANNs) to learn across a range of tasks and domains, combining and re-using learned representations where required, is a clear goal of artificial intelligence. This capacity, widely described as continual learning, has become a prolific subfield of research in machine learning. Despite the numerous successes of deep learning in recent years, across domains ranging from image recognition to machine translation, such continual task learning has proved challenging. Neural networks trained on multiple tasks in sequence with stochastic gradient descent often suffer from representational interference, whereby the learned weights for a given task effectively overwrite those of previous tasks in a process termed catastrophic forgetting. This represents a major impediment to the development of more generalised artificial learning systems, capable of accumulating knowledge over time and task space, in a manner analogous to humans. A repository of selected papers and implementations accompanying this review can be found at https://github.com/mccaffary/continual-learning.
Rediscovering Affordance: A Reinforcement Learning Perspective
Authors: Yi-Chi Liao, Kashyap Todi, Aditya Acharya, Antti Keurulainen, Andrew Howes, Antti Oulasvirta
Date: 2021-12-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Affordance refers to the perception of possible actions allowed by an object. Despite its relevance to human-computer interaction, no existing theory explains the mechanisms that underpin affordance-formation; that is, how affordances are discovered and adapted via interaction. We propose an integrative theory of affordance-formation based on the theory of reinforcement learning in cognitive sciences. The key assumption is that users learn to associate promising motor actions to percepts via experience when reinforcement signals (success/failure) are present. They also learn to categorize actions (e.g., "rotating" a dial), giving them the ability to name and reason about affordance. Upon encountering novel widgets, their ability to generalize these actions determines their ability to perceive affordances. We implement this theory in a virtual robot model, which demonstrates human-like adaptation of affordance in interactive widgets tasks. While its predictions align with trends in human data, humans are able to adapt affordances faster, suggesting the existence of additional mechanisms.
Unsupervised Domain Adaptation for Constraining Star Formation Histories
Authors: Sankalp Gilda, Antoine de Mathelin, Sabine Bellstedt, Guillaume Richard
Date: 2021-12-28 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

The prevalent paradigm of machine learning today is to use past observations to predict future ones. What if, however, we are interested in knowing the past given the present? This situation is indeed one that astronomers must contend with often. To understand the formation of our universe, we must derive the time evolution of the visible mass content of galaxies. However, to observe a complete star life, one would need to wait for one billion years! To overcome this difficulty, astrophysicists leverage supercomputers and evolve simulated models of galaxies till the current age of the universe, thus establishing a mapping between observed radiation and star formation histories (SFHs). Such ground-truth SFHs are lacking for actual galaxy observations, where they are usually inferred -- with often poor confidence -- from spectral energy distributions (SEDs) using Bayesian fitting methods. In this investigation, we discuss the ability of unsupervised domain adaptation to derive accurate SFHs for galaxies with simulated data as a necessary first step in developing a technique that can ultimately be applied to observational data.
Multilayer Graph Contrastive Clustering Network
Authors: Liang Liu, Zhao Kang, Ling Tian, Wenbo Xu, Xixu He
Date: 2021-12-28 00:00:00
Tasks: Clustering, Graph Clustering

Multilayer graph has garnered plenty of research attention in many areas due to their high utility in modeling interdependent systems. However, clustering of multilayer graph, which aims at dividing the graph nodes into categories or communities, is still at a nascent stage. Existing methods are often limited to exploiting the multiview attributes or multiple networks and ignoring more complex and richer network frameworks. To this end, we propose a generic and effective autoencoder framework for multilayer graph clustering named Multilayer Graph Contrastive Clustering Network (MGCCN). MGCCN consists of three modules: (1)Attention mechanism is applied to better capture the relevance between nodes and neighbors for better node embeddings. (2)To better explore the consistent information in different networks, a contrastive fusion strategy is introduced. (3)MGCCN employs a self-supervised component that iteratively strengthens the node embedding and clustering. Extensive experiments on different types of real-world graph data indicate that our proposed method outperforms state-of-the-art techniques.
Towards Relatable Explainable AI with the Perceptual Process
Authors: Wencan Zhang, Brian Y. Lim
Date: 2021-12-28 00:00:00
Tasks: counterfactual, Emotion Recognition, Explainable Artificial Intelligence (XAI)

Machine learning models need to provide contrastive explanations, since people often seek to understand why a puzzling prediction occurred instead of some expected outcome. Current contrastive explanations are rudimentary comparisons between examples or raw features, which remain difficult to interpret, since they lack semantic meaning. We argue that explanations must be more relatable to other concepts, hypotheticals, and associations. Inspired by the perceptual process from cognitive psychology, we propose the XAI Perceptual Processing Framework and RexNet model for relatable explainable AI with Contrastive Saliency, Counterfactual Synthetic, and Contrastive Cues explanations. We investigated the application of vocal emotion recognition, and implemented a modular multi-task deep neural network to predict and explain emotions from speech. From think-aloud and controlled studies, we found that counterfactual explanations were useful and further enhanced with semantic cues, but not saliency explanations. This work provides insights into providing and evaluating relatable contrastive explainable AI for perception applications.
Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data
Authors: Matej Grcić, Petra Bevandić, Zoran Kalafatić, Siniša Šegvić
Date: 2021-12-23 00:00:00
Tasks: Anomaly Detection, Out-of-Distribution Detection

Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions which may lead to devastating consequences. This problem is especially demanding in the context of dense prediction since input images may be only partially anomalous. Previous work has addressed dense out-of-distribution detection by discriminative training with respect to off-the-shelf negative datasets. However, real negative data are unlikely to cover all modes of the entire visual world. To this end, we extend this approach by generating synthetic negative patches along the border of the inlier manifold. We leverage a jointly trained normalizing flow due to coverage-oriented learning objective and the capability to generate samples at different resolutions. We detect anomalies according to a principled information-theoretic criterion which can be consistently applied through training and inference. The resulting models set the new state of the art on benchmarks for out-of-distribution detection in road-driving scenes and remote sensing imagery, in spite of minimal computational overhead.
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
Authors: Angeliki Kamoutsi, Goran Banjac, John Lygeros
Date: 2021-12-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider large-scale Markov decision processes with an unknown cost function and address the problem of learning a policy from a finite set of expert demonstrations. We assume that the learner is not allowed to interact with the expert and has no access to reinforcement signal of any kind. Existing inverse reinforcement learning methods come with strong theoretical guarantees, but are computationally expensive, while state-of-the-art policy optimization algorithms achieve significant empirical success, but are hampered by limited theoretical understanding. To bridge the gap between theory and practice, we introduce a novel bilinear saddle-point framework using Lagrangian duality. The proposed primal-dual viewpoint allows us to develop a model-free provably efficient algorithm through the lens of stochastic convex optimization. The method enjoys the advantages of simplicity of implementation, low memory requirements, and computational and sample complexities independent of the number of states. We further present an equivalent no-regret online-learning interpretation.
Relative velocity-based reward functions for crowd navigation of robots
Authors: Xiaoqing Yang, Fei Li
Date: 2021-12-28 00:00:00
Tasks: Electrical Engineering, Task Planning

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment, but while the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the appli-cable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the model is 90% accurate. The results of energy consumption modeling can help robots to save energy by helping them to perform rational path planning and task planning.
Can Social Ontological Knowledge Representations be Measured Using Machine Learning?
Authors: Ahmed Izzidien
Date: 2021-12-27 00:00:00
Tasks: BIG-bench Machine Learning, valid

Personal Social Ontology (PSO), it is proposed, is how an individual perceives the ontological properties of terms. For example, an absolute fatalist would arguably use terms that remove any form of agency from a person. Such fatalism has the impact of ontologically defining acts such as winning, victory and success in a manner that is contrary to how a non-fatalist would ontologically define them. While both the said fatalist and non-fatalist would agree on the dictionary definition of these terms, they would differ on specifically how they can be brought about. This difference between the two individuals can be induced from their usage of these terms, i.e., the co-occurrence of these terms with other terms. As such a quantification of this such co-occurrence offers an avenue to characterise the social ontological views of the speaker. In this paper we ask, what specific term co-occurrence should be measured in order to obtain a valid and reliable psychometric measure of a persons social ontology? We consider the social psychology and social neuroscience literature to arrive at a list of social concepts that can be considered principal features of personal social ontology, and then propose an NLP pipeline to capture the articulation of these terms in language.
A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning
Authors: Akansha Singh Bansal, Trapit Bansal, David Irwin
Date: 2021-12-28 00:00:00
Tasks: Self-Supervised Learning

Solar energy is now the cheapest form of electricity in history. Unfortunately, significantly increasing the grid's fraction of solar energy remains challenging due to its variability, which makes balancing electricity's supply and demand more difficult. While thermal generators' ramp rate -- the maximum rate that they can change their output -- is finite, solar's ramp rate is essentially infinite. Thus, accurate near-term solar forecasting, or nowcasting, is important to provide advance warning to adjust thermal generator output in response to solar variations to ensure a balanced supply and demand. To address the problem, this paper develops a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Specifically, we develop deep auto-regressive models using convolutional neural networks (CNN) and long short-term memory networks (LSTM) that are globally trained across multiple locations to predict raw future observations of the spatio-temporal data collected by the recently launched GOES-R series of satellites. Our model estimates a location's future solar irradiance based on satellite observations, which we feed to a regression model trained on smaller site-specific solar data to provide near-term solar photovoltaic (PV) forecasts that account for site-specific characteristics. We evaluate our approach for different coverage areas and forecast horizons across 25 solar sites and show that our approach yields errors close to that of a model using ground-truth observations.
HiKonv: High Throughput Quantized Convolution With Novel Bit-wise Management and Computation
Authors: Xinheng Liu, Yao Chen, Prakhar Ganesh, Junhao Pan, JinJun Xiong, Deming Chen
Date: 2021-12-28 00:00:00
Tasks: CPU, Management, Quantization

Quantization for Convolutional Neural Network (CNN) has shown significant progress with the intention of reducing the cost of computation and storage with low-bitwidth data inputs. There are, however, no systematic studies on how an existing full-bitwidth processing unit, such as CPUs and DSPs, can be better utilized to carry out significantly higher computation throughput for convolution under various quantized bitwidths. In this study, we propose HiKonv, a unified solution that maximizes the compute throughput of a given underlying processing unit to process low-bitwidth quantized data inputs through novel bit-wise parallel computation. We establish theoretical performance bounds using a full-bitwidth multiplier for highly parallelized low-bitwidth convolution, and demonstrate new breakthroughs for high-performance computing in this critical domain. For example, a single 32-bit processing unit can deliver 128 binarized convolution operations (multiplications and additions) under one CPU instruction, and a single 27x18 DSP core can deliver eight convolution operations with 4-bit inputs in one cycle. We demonstrate the effectiveness of HiKonv on CPU and FPGA for both convolutional layers or a complete DNN model. For a convolutional layer quantized to 4-bit, HiKonv achieves a 3.17x latency improvement over the baseline implementation using C++ on CPU. Compared to the DAC-SDC 2020 champion model for FPGA, HiKonv achieves a 2.37x throughput improvement and 2.61x DSP efficiency improvement, respectively.
SiGMa: Simple Greedy Matching for Aligning Large Knowledge Bases
Authors: Simon Lacoste-Julien, Konstantina Palla, Alex Davies, Gjergji Kasneci, Thore Graepel, Zoubin Ghahramani
Date: 2012-07-19 00:00:00

The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm which leverages both the structural information from the relationship graph as well as flexible similarity measures between entity properties in a greedy local search, thus making it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world's largest knowledge bases with high precision. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency.
A Conformal Prediction Approach to Explore Functional Data
Authors: Jing Lei, Alessandro Rinaldo, Larry Wasserman
Date: 2013-02-26 00:00:00
Tasks: Clustering, Conformal Prediction, Prediction

This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data. These tools can be used to detect outliers and clusters. Both our prediction bands and clustering trees provide prediction sets for the underlying stochastic process with a guaranteed finite sample behavior, under no distributional assumptions. The prediction sets are also informative in that they correspond to the high density region of the underlying process. While ordinary conformal prediction has high computational cost for functional data, we use the inductive conformal predictor, together with several novel choices of conformity scores, to simplify the computation. Our methods are illustrated on some real data examples.
Invertible Image Dataset Protection
Authors: Kejiang Chen, Xianhan Zeng, Qichao Ying, Sheng Li, Zhenxing Qian, Xinpeng Zhang
Date: 2021-12-29 00:00:00
Tasks: Adversarial Defense

Deep learning has achieved enormous success in various industrial applications. Companies do not want their valuable data to be stolen by malicious employees to train pirated models. Nor do they wish the data analyzed by the competitors after using them online. We propose a novel solution for dataset protection in this scenario by robustly and reversibly transform the images into adversarial images. We develop a reversible adversarial example generator (RAEG) that introduces slight changes to the images to fool traditional classification models. Even though malicious attacks train pirated models based on the defensed versions of the protected images, RAEG can significantly weaken the functionality of these models. Meanwhile, the reversibility of RAEG ensures the performance of authorized models. Extensive experiments demonstrate that RAEG can better protect the data with slight distortion against adversarial defense than previous methods.