pwc-archive/papers-with-abstracts huggingface dataset:
Fair Decision-Making for Food Inspections
Authors: Shubham Singh, Bhuvni Shah, Chris Kanich, Ian A. Kash
Date: 2021-08-12 00:00:00
Tasks: Decision Making, Fairness, Scheduling

Data and algorithms are essential and complementary parts of a large-scale decision-making process. However, their injudicious use can lead to unforeseen consequences, as has been observed by researchers and activists alike in the recent past. In this paper, we revisit the application of predictive models by the Chicago Department of Public Health to schedule restaurant inspections and prioritize the detection of critical food code violations. We perform the first analysis of the model's fairness to the population served by the restaurants in terms of average time to find a critical violation. We find that the model treats inspections unequally based on the sanitarian who conducted the inspection and that, in turn, there are geographic disparities in the benefits of the model. We examine four alternate methods of model training and two alternative ways of scheduling using the model and find that the latter generate more desirable results. The challenges from this application point to important directions for future work around fairness with collective entities rather than individuals, the use of critical violations as a proxy, and the disconnect between fair classification and fairness in the dynamic scheduling system.
Reinforcement Learning Approach to Active Learning for Image Classification
Authors: Thorben Werner
Date: 2021-08-12 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Classification, image-classification, Image Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Machine Learning requires large amounts of labeled data to fit a model. Many datasets are already publicly available, nevertheless forcing application possibilities of machine learning to the domains of those public datasets. The ever-growing penetration of machine learning algorithms in new application areas requires solutions for the need for data in those new domains. This thesis works on active learning as one possible solution to reduce the amount of data that needs to be processed by hand, by processing only those datapoints that specifically benefit the training of a strong model for the task. A newly proposed framework for framing the active learning workflow as a reinforcement learning problem is adapted for image classification and a series of three experiments is conducted. Each experiment is evaluated and potential issues with the approach are outlined. Each following experiment then proposes improvements to the framework and evaluates their impact. After the last experiment, a final conclusion is drawn, unfortunately rejecting this work's hypothesis and outlining that the proposed framework at the moment is not capable of improving active learning for image classification with a trained reinforcement learning agent.
AffRankNet+: Ranking Affect Using Privileged Information
Authors: Konstantinos Makantasis
Date: 2021-08-12 00:00:00

Many of the affect modelling tasks present an asymmetric distribution of information between training and test time; additional information is given about the training data, which is not available at test time. Learning under this setting is called Learning Under Privileged Information (LUPI). At the same time, due to the ordinal nature of affect annotations, formulating affect modelling tasks as supervised learning ranking problems is gaining ground within the Affective Computing research community. Motivated by the two facts above, in this study, we introduce a ranking model that treats additional information about the training data as privileged information to accurately rank affect states. Our ranking model extends the well-known RankNet model to the LUPI paradigm, hence its name AffRankNet+. To the best of our knowledge, it is the first time that a ranking model based on neural networks exploits privileged information. We evaluate the performance of the proposed model on the public available Afew-VA dataset and compare it against the RankNet model, which does not use privileged information. Experimental evaluation indicates that the AffRankNet+ model can yield significantly better performance.
Dynamic Attention-based Communication-Efficient Federated Learning
Authors: Zihan Chen, Kai Fong Ernest Chong, Tony Q. S. Quek
Date: 2021-08-12 00:00:00
Tasks: Federated Learning

Federated learning (FL) offers a solution to train a global machine learning model while still maintaining data privacy, without needing access to data stored locally at the clients. However, FL suffers performance degradation when client data distribution is non-IID, and a longer training duration to combat this degradation may not necessarily be feasible due to communication limitations. To address this challenge, we propose a new adaptive training algorithm $\texttt{AdaFL}$, which comprises two components: (i) an attention-based client selection mechanism for a fairer training scheme among the clients; and (ii) a dynamic fraction method to balance the trade-off between performance stability and communication efficiency. Experimental results show that our $\texttt{AdaFL}$ algorithm outperforms the usual $\texttt{FedAvg}$ algorithm, and can be incorporated to further improve various state-of-the-art FL algorithms, with respect to three aspects: model accuracy, performance stability, and communication efficiency.
Learning from Matured Dumb Teacher for Fine Generalization
Authors: HeeSeung Jung, Kangil Kim, Hoyong Kim, Jong-Hun Shin
Date: 2021-08-12 00:00:00
Tasks: image-classification, Image Classification, Knowledge Distillation

The flexibility of decision boundaries in neural networks that are unguided by training data is a well-known problem typically resolved with generalization methods. A surprising result from recent knowledge distillation (KD) literature is that random, untrained, and equally structured teacher networks can also vastly improve generalization performance. It raises the possibility of existence of undiscovered assumptions useful for generalization on an uncertain region. In this paper, we shed light on the assumptions by analyzing decision boundaries and confidence distributions of both simple and KD-based generalization methods. Assuming that a decision boundary exists to represent the most general tendency of distinction on an input sample space (i.e., the simplest hypothesis), we show the various limitations of methods when using the hypothesis. To resolve these limitations, we propose matured dumb teacher based KD, conservatively transferring the hypothesis for generalization of the student without massive destruction of trained information. In practical experiments on feed-forward and convolution neural networks for image classification tasks on MNIST, CIFAR-10, and CIFAR-100 datasets, the proposed method shows stable improvement to the best test performance in the grid search of hyperparameters. The analysis and results imply that the proposed method can provide finer generalization than existing methods.
PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management
Authors: Jiarui Fang, Zilin Zhu, Shenggui Li, Hui Su, Yang Yu, Jie zhou, Yang You
Date: 2021-08-12 00:00:00
Tasks: CPU, GPU, Management

The pre-trained model (PTM) is revolutionizing Artificial Intelligence (AI) technology. However, the hardware requirement of PTM training is prohibitively high, making it a game for a small proportion of people. Therefore, we proposed PatrickStar system to lower the hardware requirements of PTMs and make them accessible to everyone. PatrickStar uses the CPU-GPU heterogeneous memory space to store the model data. Different from existing works, we organize the model data in memory chunks and dynamically distribute them in the heterogeneous memory. Guided by the runtime memory statistics collected in a warm-up iteration, chunks are orchestrated efficiently in heterogeneous memory and generate lower CPU-GPU data transmission volume and higher bandwidth utilization. Symbiosis with the Zero Redundancy Optimizer, PatrickStar scales to multiple GPUs on multiple nodes. % using data parallelism. The system can train tasks on bigger models and larger batch sizes, which cannot be accomplished by existing works. Experimental results show that PatrickStar extends model scales 2.27 and 2.5 times of DeepSpeed, and consistently exhibits significantly higher execution speed. PatricStar also successfully runs the 175B GPT3 training task on a 32 GPU cluster. Our code is publicly available at https://github.com/Tencent/PatrickStar.
Seismic wave propagation and inversion with Neural Operators
Authors: Yan Yang, Angela F. Gao, Jorge C. Castellanos, Zachary E. Ross, Kamyar Azizzadenesheli, Robert W. Clayton
Date: 2021-08-11 00:00:00
Tasks: Computational Efficiency

Seismic wave propagation forms the basis for most aspects of seismological research, yet solving the wave equation is a major computational burden that inhibits the progress of research. This is exacerbated by the fact that new simulations must be performed when the velocity structure or source location is perturbed. Here, we explore a prototype framework for learning general solutions using a recently developed machine learning paradigm called Neural Operator. A trained Neural Operator can compute a solution in negligible time for any velocity structure or source location. We develop a scheme to train Neural Operators on an ensemble of simulations performed with random velocity models and source locations. As Neural Operators are grid-free, it is possible to evaluate solutions on higher resolution velocity models than trained on, providing additional computational efficiency. We illustrate the method with the 2D acoustic wave equation and demonstrate the method's applicability to seismic tomography, using reverse mode automatic differentiation to compute gradients of the wavefield with respect to the velocity structure. The developed procedure is nearly an order of magnitude faster than using conventional numerical methods for full waveform inversion.
Learning to Hash Robustly, Guaranteed
Authors: Alexandr Andoni, Daniel Beaglehole
Date: 2021-08-11 00:00:00

The indexing algorithms for the high-dimensional nearest neighbor search (NNS) with the best worst-case guarantees are based on the randomized Locality Sensitive Hashing (LSH), and its derivatives. In practice, many heuristic approaches exist to "learn" the best indexing method in order to speed-up NNS, crucially adapting to the structure of the given dataset. Oftentimes, these heuristics outperform the LSH-based algorithms on real datasets, but, almost always, come at the cost of losing the guarantees of either correctness or robust performance on adversarial queries, or apply to datasets with an assumed extra structure/model. In this paper, we design an NNS algorithm for the Hamming space that has worst-case guarantees essentially matching that of theoretical algorithms, while optimizing the hashing to the structure of the dataset (think instance-optimal algorithms) for performance on the minimum-performing query. We evaluate the algorithm's ability to optimize for a given dataset both theoretically and practically. On the theoretical side, we exhibit a natural setting (dataset model) where our algorithm is much better than the standard theoretical one. On the practical side, we run experiments that show that our algorithm has a 1.8x and 2.1x better recall on the worst-performing queries to the MNIST and ImageNet datasets.
A Contract Theory based Incentive Mechanism for Federated Learning
Authors: Mengmeng Tian, Yuxin Chen, YuAn Liu, Zehui Xiong, Cyril Leung, Chunyan Miao
Date: 2021-08-12 00:00:00
Tasks: Federated Learning

Federated learning (FL) serves as a data privacy-preserved machine learning paradigm, and realizes the collaborative model trained by distributed clients. To accomplish an FL task, the task publisher needs to pay financial incentives to the FL server and FL server offloads the task to the contributing FL clients. It is challenging to design proper incentives for the FL clients due to the fact that the task is privately trained by the clients. This paper aims to propose a contract theory based FL task training model towards minimizing incentive budget subject to clients being individually rational (IR) and incentive compatible (IC) in each FL training round. We design a two-dimensional contract model by formally defining two private types of clients, namely data quality and computation effort. To effectively aggregate the trained models, a contract-based aggregator is proposed. We analyze the feasible and optimal contract solutions to the proposed contract model. %Experimental results demonstrate that the proposed framework and contract model can effective improve the generation accuracy of FL tasks. Experimental results show that the generalization accuracy of the FL tasks can be improved by the proposed incentive mechanism where contract-based aggregation is applied.
RW-Resnet: A Novel Speech Anti-Spoofing Model Using Raw Waveform
Authors: Youxuan Ma, Zongze Ren, Shugong Xu
Date: 2021-08-12 00:00:00
Tasks: Speaker Verification, Synthetic Speech Detection, text-to-speech, Text to Speech, Voice Conversion

In recent years, synthetic speech generated by advanced text-to-speech (TTS) and voice conversion (VC) systems has caused great harms to automatic speaker verification (ASV) systems, urging us to design a synthetic speech detection system to protect ASV systems. In this paper, we propose a new speech anti-spoofing model named ResWavegram-Resnet (RW-Resnet). The model contains two parts, Conv1D Resblocks and backbone Resnet34. The Conv1D Resblock is based on the Conv1D block with a residual connection. For the first part, we use the raw waveform as input and feed it to the stacked Conv1D Resblocks to get the ResWavegram. Compared with traditional methods, ResWavegram keeps all the information from the audio signal and has a stronger ability in extracting features. For the second part, the extracted features are fed to the backbone Resnet34 for the spoofed or bonafide decision. The ASVspoof2019 logical access (LA) corpus is used to evaluate our proposed RW-Resnet. Experimental results show that the RW-Resnet achieves better performance than other state-of-the-art anti-spoofing models, which illustrates its effectiveness in detecting synthetic speech attacks.
Correlation Clustering with Asymmetric Classification Errors
Authors: Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev
Date: 2021-08-11 00:00:00
Proceeding: ICML 2020 1
Tasks: Classification, Clustering

In the Correlation Clustering problem, we are given a weighted graph $G$ with its edges labeled as "similar" or "dissimilar" by a binary classifier. The goal is to produce a clustering that minimizes the weight of "disagreements": the sum of the weights of "similar" edges across clusters and "dissimilar" edges within clusters. We study the correlation clustering problem under the following assumption: Every "similar" edge $e$ has weight $\mathbf{w}_e\in[\alpha \mathbf{w}, \mathbf{w}]$ and every "dissimilar" edge $e$ has weight $\mathbf{w}_e\geq \alpha \mathbf{w}$ (where $\alpha\leq 1$ and $\mathbf{w}>0$ is a scaling parameter). We give a $(3 + 2 \log_e (1/\alpha))$ approximation algorithm for this problem. This assumption captures well the scenario when classification errors are asymmetric. Additionally, we show an asymptotically matching Linear Programming integrality gap of $\Omega(\log 1/\alpha)$.
Local Correlation Clustering with Asymmetric Classification Errors
Authors: Jafar Jafarov, Sanchit Kalhan, Konstantin Makarychev, Yury Makarychev
Date: 2021-08-11 00:00:00
Tasks: Classification, Clustering

In the Correlation Clustering problem, we are given a complete weighted graph $G$ with its edges labeled as "similar" and "dissimilar" by a noisy binary classifier. For a clustering $\mathcal{C}$ of graph $G$, a similar edge is in disagreement with $\mathcal{C}$, if its endpoints belong to distinct clusters; and a dissimilar edge is in disagreement with $\mathcal{C}$ if its endpoints belong to the same cluster. The disagreements vector, $\text{dis}$, is a vector indexed by the vertices of $G$ such that the $v$-th coordinate $\text{dis}_v$ equals the weight of all disagreeing edges incident on $v$. The goal is to produce a clustering that minimizes the $\ell_p$ norm of the disagreements vector for $p\geq 1$. We study the $\ell_p$ objective in Correlation Clustering under the following assumption: Every similar edge has weight in the range of $[\alpha\mathbf{w},\mathbf{w}]$ and every dissimilar edge has weight at least $\alpha\mathbf{w}$ (where $\alpha \leq 1$ and $\mathbf{w}>0$ is a scaling parameter). We give an $O\left((\frac{1}{\alpha})^{\frac{1}{2}-\frac{1}{2p}}\cdot \log\frac{1}{\alpha}\right)$ approximation algorithm for this problem. Furthermore, we show an almost matching convex programming integrality gap.
Multimodal analysis of the predictability of hand-gesture properties
Authors: Taras Kucherenko, Rajmund Nagy, Michael Neff, Hedvig Kjellström, Gustav Eje Henter
Date: 2021-08-12 00:00:00
Tasks: Gesture Generation, Rhythm

Embodied conversational agents benefit from being able to accompany their speech with gestures. Although many data-driven approaches to gesture generation have been proposed in recent years, it is still unclear whether such systems can consistently generate gestures that convey meaning. We investigate which gesture properties (phase, category, and semantics) can be predicted from speech text and/or audio using contemporary deep learning. In extensive experiments, we show that gesture properties related to gesture meaning (semantics and category) are predictable from text features (time-aligned FastText embeddings) alone, but not from prosodic audio features, while rhythm-related gesture properties (phase) on the other hand can be predicted from audio features better than from text. These results are encouraging as they indicate that it is possible to equip an embodied agent with content-wise meaningful co-speech gestures using a machine-learning model.
Goal scoring in Premier League with Poisson regression
Authors: Cuong Pham, Tung Le
Date: 2021-07-10 00:00:00
Tasks: regression, Relation

Premier League is known as one of the most competitive football league in the world, hence there are many goals are scored here every match. Which are the factors that affect to the number of goal scored in each match? We use Poisson regression to find out the relation between many factors as shots on target, corners, red cards, to the goals home team can score in their match.
Decoder Fusion RNN: Context and Interaction Aware Decoders for Trajectory Prediction
Authors: Edoardo Mello Rella, Jan-Nico Zaech, Alexander Liniger, Luc van Gool
Date: 2021-08-12 00:00:00
Tasks: Autonomous Driving, Decoder, Motion Forecasting, Trajectory Prediction

Forecasting the future behavior of all traffic agents in the vicinity is a key task to achieve safe and reliable autonomous driving systems. It is a challenging problem as agents adjust their behavior depending on their intentions, the others' actions, and the road layout. In this paper, we propose Decoder Fusion RNN (DF-RNN), a recurrent, attention-based approach for motion forecasting. Our network is composed of a recurrent behavior encoder, an inter-agent multi-headed attention module, and a context-aware decoder. We design a map encoder that embeds polyline segments, combines them to create a graph structure, and merges their relevant parts with the agents' embeddings. We fuse the encoded map information with further inter-agent interactions only inside the decoder and propose to use explicit training as a method to effectively utilize the information available. We demonstrate the efficacy of our method by testing it on the Argoverse motion forecasting dataset and show its state-of-the-art performance on the public benchmark.
Speed Control of DC Motor Using Fuzzy PID Controller
Authors: Messaadi Nassim, Amroun Abdelkader
Date: 2021-08-11 00:00:00

In this project, we designed a DC motor whose speed can be controlled by a PID controller. The proportional, integral and derivative gains (KP, KI, KD) of the PID controller are adjusted according to Fuzzy logic. First of all, the fuzzy logic controller is designed according to rules so that the systems is basically robust. There are 25 rules for the auto-tuning of each parameter of the PID controller. The FLC (fuzzy logic controller) has two inputs. The first is the motor speed error between the reference (setpoint) and the actual speed. The second is the variation of the speed error (derivative of the speed error). Secondly the output of the FLC is the parameters of the PID controller which are used to control the speed of the DC motor. The study shows that both the precise characters of PID controllers and the flexible characters of fuzzy controllers are present in the fuzzy self-tuning PID controller. The fuzzy auto-tuning approach implemented on a conventional PID structure was able to control the speed of the DC motor. It also improved the dynamic and static response of the system. The comparison between the conventional response and the fuzzy self-tuning response was performed based on the simulation result obtained by MATLAB/SIMULINK. The simulation results show that the designed self-adaptive PID controller achieves good dynamic behavior of the DC motor, perfect speed tracking with short rise and settling times, zero overshoot and steady state error and thus gives better performance compared to the conventional PID controller. We then model the fuzzy PID using simple code on Arduino IDE and perform a practical experiment, to confirm our theorical results.
Xi-Vector Embedding for Speaker Recognition
Authors: Kong Aik Lee, Qiongqiong Wang, Takafumi Koshinaka
Date: 2021-08-12 00:00:00
Tasks: Speaker Recognition

We present a Bayesian formulation for deep speaker embedding, wherein the xi-vector is the Bayesian counterpart of the x-vector, taking into account the uncertainty estimate. On the technology front, we offer a simple and straightforward extension to the now widely used x-vector. It consists of an auxiliary neural net predicting the frame-wise uncertainty of the input sequence. We show that the proposed extension leads to substantial improvement across all operating points, with a significant reduction in error rates and detection cost. On the theoretical front, our proposal integrates the Bayesian formulation of linear Gaussian model to speaker-embedding neural networks via the pooling layer. In one sense, our proposal integrates the Bayesian formulation of the i-vector to that of the x-vector. Hence, we refer to the embedding as the xi-vector, which is pronounced as /zai/ vector. Experimental results on the SITW evaluation set show a consistent improvement of over 17.5% in equal-error-rate and 10.9% in minimum detection cost.