pwc-archive/papers-with-abstracts huggingface dataset:
Analytically Tractable Hidden-States Inference in Bayesian Neural Networks
Authors: Luong-Ha Nguyen, James-A. Goulet
Date: 2021-07-08 00:00:00
Tasks: Adversarial Attack, Bayesian Inference

With few exceptions, neural networks have been relying on backpropagation and gradient descent as the inference engine in order to learn the model parameters, because the closed-form Bayesian inference for neural networks has been considered to be intractable. In this paper, we show how we can leverage the tractable approximate Gaussian inference's (TAGI) capabilities to infer hidden states, rather than only using it for inferring the network's parameters. One novel aspect it allows is to infer hidden states through the imposition of constraints designed to achieve specific objectives, as illustrated through three examples: (1) the generation of adversarial-attack examples, (2) the usage of a neural network as a black-box optimization method, and (3) the application of inference on continuous-action reinforcement learning. These applications showcase how tasks that were previously reserved to gradient-based optimization approaches can now be approached with analytically tractable inference
Adaptation of Quadruped Robot Locomotion with Meta-Learning
Authors: Arsen Kuzhamuratov, Dmitry Sorokin, Alexander Ulanov, A. I. Lvovsky
Date: 2021-07-08 00:00:00
Tasks: Meta-Learning, Meta Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Animals have remarkable abilities to adapt locomotion to different terrains and tasks. However, robots trained by means of reinforcement learning are typically able to solve only a single task and a transferred policy is usually inferior to that trained from scratch. In this work, we demonstrate that meta-reinforcement learning can be used to successfully train a robot capable to solve a wide range of locomotion tasks. The performance of the meta-trained robot is similar to that of a robot that is trained on a single task.
Direct detection of plasticity onset through total-strain profile evolution
Authors: Stefanos Papanikolaou, Mikko J. Alava
Date: 2021-07-08 00:00:00

Plastic yielding in solids strongly depends on various conditions, such as temperature and loading rate and indeed, sample-dependent knowledge of yield points in structural materials promotes reliability in mechanical behavior. Commonly, yielding is measured through controlled mechanical testing at small or large scales, in ways that either distinguish elastic (stress) from total deformation measurements, or by identifying plastic slip contributions. In this paper we argue that instead of separate elastic/plastic measurements, yielding can be unraveled through statistical analysis of total strain fluctuations during the evolution sequence of profiles measured in-situ, through digital image correlation. We demonstrate two distinct ways of precisely quantifying yield locations in widely applicable crystal plasticity models, that apply in polycrystalline solids, either by using principal component analysis or discrete wavelet transforms. We test and compare these approaches in synthetic data of polycrystal simulations and a variety of yielding responses, through changes of the applied loading rates and the strain-rate sensitivity exponents.
Encoding Domain Information with Sparse Priors for Inferring Explainable Latent Variables
Authors: Arber Qoku, Florian Buettner
Date: 2021-07-08 00:00:00

Latent variable models are powerful statistical tools that can uncover relevant variation between patients or cells, by inferring unobserved hidden states from observable high-dimensional data. A major shortcoming of current methods, however, is their inability to learn sparse and interpretable hidden states. Additionally, in settings where partial knowledge on the latent structure of the data is readily available, a statistically sound integration of prior information into current methods is challenging. To address these issues, we propose spex-LVM, a factorial latent variable model with sparse priors to encourage the inference of explainable factors driven by domain-relevant information. spex-LVM utilizes existing knowledge of curated biomedical pathways to automatically assign annotated attributes to latent factors, yielding interpretable results tailored to the corresponding domain of interest. Evaluations on simulated and real single-cell RNA-seq datasets demonstrate that our model robustly identifies relevant structure in an inherently explainable manner, distinguishes technical noise from sources of biomedical variation, and provides dataset-specific adaptations of existing pathway annotations. Implementation is available at https://github.com/MLO-lab/spexlvm.
The Three Ensemble Clustering (3EC) Algorithm for Pattern Discovery in Unsupervised Learning
Authors: Kundu, Debasish
Date: 2021-07-08 00:00:00
Tasks: Clustering

This paper presents a multiple learner algorithm called the 'Three Ensemble Clustering 3EC' algorithm that classifies unlabeled data into quality clusters as a part of unsupervised learning. It offers the flexibility to explore the context of new clusters formed by an ensemble of algorithms based on internal validation indices. It is worth mentioning that the input data set is considered to be a cluster of clusters. An anomaly can possibly manifest as a cluster as well. Each partitioned cluster is considered to be a new data set and is a candidate to explore the most optimal algorithm and its number of partition splits until a predefined stopping criteria is met. The algorithms independently partition the data set into clusters and the quality of the partitioning is assessed by an ensemble of internal cluster validation indices. The 3EC algorithm presents the validation index scores from a choice of algorithms and its configuration of partitions and it is called the Tau Grid. 3EC chooses the most optimal score. The 3EC algorithm owes its name to the two input ensembles of algorithms and internal validation indices and an output ensemble of final clusters. Quality plays an important role in this clustering approach and it also acts as a stopping criteria from further partitioning. Quality is determined based on the quality of the clusters provided by an algorithm and its optimal number of splits. The 3EC algorithm determines this from the score of the ensemble of validation indices. The user can configure the stopping criteria by providing quality thresholds for the score range of each of the validation indices and the optimal size of the output cluster. The users can experiment with different sets of stopping criteria and choose the most 'sensible group' of quality clusters
Digitizing Handwriting with a Sensor Pen: A Writer-Independent Recognizer
Authors: Mohamad Wehbi, Tim Hamann, Jens Barth, Bjoern Eskofier
Date: 2021-07-08 00:00:00
Tasks: Handwriting Recognition

Online handwriting recognition has been studied for a long time with only few practicable results when writing on normal paper. Previous approaches using sensor-based devices encountered problems that limited the usage of the developed systems in real-world applications. This paper presents a writer-independent system that recognizes characters written on plain paper with the use of a sensor-equipped pen. This system is applicable in real-world applications and requires no user-specific training for recognition. The pen provides linear acceleration, angular velocity, magnetic field, and force applied by the user, and acts as a digitizer that transforms the analogue signals of the sensors into timeseries data while writing on regular paper. The dataset we collected with this pen consists of Latin lower-case and upper-case alphabets. We present the results of a convolutional neural network model for letter classification and show that this approach is practical and achieves promising results for writer-independent character recognition. This work aims at providing a realtime handwriting recognition system to be used for writing on normal paper.
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
Authors: Michael A. Hedderich, Benjamin Roth, Katharina Kann, Barbara Plank, Alex Ratner, Dietrich Klakow
Date: 2021-07-08 00:00:00
Tasks: Weakly-supervised Learning

Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning, co-located with ICLR 2021. In this workshop, we want to advance theory, methods and tools for allowing experts to express prior coded knowledge for automatic data annotations that can be used to train arbitrary deep neural networks for prediction. The ICLR 2021 Workshop on Weak Supervision aims at advancing methods that help modern machine-learning methods to generalize from knowledge provided by experts, in interaction with observable (unlabeled) data. In total, 15 papers were accepted. All the accepted contributions are listed in these Proceedings.
MOD-Net: A Machine Learning Approach via Model-Operator-Data Network for Solving PDEs
Authors: Lulu Zhang, Tao Luo, Yaoyu Zhang, Weinan E, Zhi-Qin John Xu, Zheng Ma
Date: 2021-07-08 00:00:00

In this paper, we propose a a machine learning approach via model-operator-data network (MOD-Net) for solving PDEs. A MOD-Net is driven by a model to solve PDEs based on operator representation with regularization from data. For linear PDEs, we use a DNN to parameterize the Green's function and obtain the neural operator to approximate the solution according to the Green's method. To train the DNN, the empirical risk consists of the mean squared loss with the least square formulation or the variational formulation of the governing equation and boundary conditions. For complicated problems, the empirical risk also includes a few labels, which are computed on coarse grid points with cheap computation cost and significantly improves the model accuracy. Intuitively, the labeled dataset works as a regularization in addition to the model constraints. The MOD-Net solves a family of PDEs rather than a specific one and is much more efficient than original neural operator because few expensive labels are required. We numerically show MOD-Net is very efficient in solving Poisson equation and one-dimensional radiative transfer equation. For nonlinear PDEs, the nonlinear MOD-Net can be similarly used as an ansatz for solving nonlinear PDEs, exemplified by solving several nonlinear PDE problems, such as the Burgers equation.
MAFIA: Machine Learning Acceleration on FPGAs for IoT Applications
Authors: Nikhil Pratap Ghanathe, Vivek Seshadri, Rahul Sharma, Steve Wilton, Aayan Kumar
Date: 2021-07-08 00:00:00
Tasks: BIG-bench Machine Learning

Recent breakthroughs in ML have produced new classes of models that allow ML inference to run directly on milliwatt-powered IoT devices. On one hand, existing ML-to-FPGA compilers are designed for deep neural-networks on large FPGAs. On the other hand, general-purpose HLS tools fail to exploit properties specific to ML inference, thereby resulting in suboptimal performance. We propose MAFIA, a tool to compile ML inference on small form-factor FPGAs for IoT applications. MAFIA provides native support for linear algebra operations and can express a variety of ML algorithms, including state-of-the-art models. We show that MAFIA-generated programs outperform best-performing variant of a commercial HLS compiler by 2.5x on average.
Assessment of hybrid machine learning models for non-linear system identification of fatigue test rigs
Authors: Leonhard Heindel, Peter Hantschke, Markus Kästner
Date: 2021-07-08 00:00:00
Tasks: Hybrid Machine Learning

The prediction of system responses for a given fatigue test bench drive signal is a challenging task, for which linear frequency response function models are commonly used. To account for non-linear phenomena, a novel hybrid model is suggested, which augments existing approaches using Long Short-Term Memory networks. Additional virtual sensing applications of this method are demonstrated. The approach is tested using non-linear experimental data from a servo-hydraulic test rig and this dataset is made publicly available. A variety of metrics in time and frequency domains, as well as fatigue strength under variable amplitudes, are employed in the evaluation.
Sublinear Regret for Learning POMDPs
Authors: Yi Xiong, Ningyuan Chen, Xuefeng Gao, Xiang Zhou
Date: 2021-07-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We study the model-based undiscounted reinforcement learning for partially observable Markov decision processes (POMDPs). The oracle we consider is the optimal policy of the POMDP with a known environment in terms of the average reward over an infinite horizon. We propose a learning algorithm for this problem, building on spectral method-of-moments estimations for hidden Markov models, the belief error control in POMDPs and upper-confidence-bound methods for online learning. We establish a regret bound of $O(T^{2/3}\sqrt{\log T})$ for the proposed learning algorithm where $T$ is the learning horizon. This is, to the best of our knowledge, the first algorithm achieving sublinear regret with respect to our oracle for learning general POMDPs.
Generalization Error of GAN from the Discriminator's Perspective
Authors: Hongkang Yang, Weinan E
Date: 2021-07-08 00:00:00
Tasks: Generative Adversarial Network, Memorization

The generative adversarial network (GAN) is a well-known model for learning high-dimensional distributions, but the mechanism for its generalization ability is not understood. In particular, GAN is vulnerable to the memorization phenomenon, the eventual convergence to the empirical distribution. We consider a simplified GAN model with the generator replaced by a density, and analyze how the discriminator contributes to generalization. We show that with early stopping, the generalization error measured by Wasserstein metric escapes from the curse of dimensionality, despite that in the long term, memorization is inevitable. In addition, we present a hardness of learning result for WGAN.
Predicting Disease Progress with Imprecise Lab Test Results
Authors: Mei Wang, Jianwen Su, Zhihua Lin
Date: 2021-07-08 00:00:00
Tasks: Prediction

In existing deep learning methods, almost all loss functions assume that sample data values used to be predicted are the only correct ones. This assumption does not hold for laboratory test data. Test results are often within tolerable or imprecision ranges, with all values in the ranges acceptable. By considering imprecision samples, we propose an imprecision range loss (IR loss) method and incorporate it into Long Short Term Memory (LSTM) model for disease progress prediction. In this method, each sample in imprecision range space has a certain probability to be the real value, participating in the loss calculation. The loss is defined as the integral of the error of each point in the impression range space. A sampling method for imprecision space is formulated. The continuous imprecision space is discretized, and a sequence of imprecise data sets are obtained, which is convenient for gradient descent learning. A heuristic learning algorithm is developed to learn the model parameters based on the imprecise data sets. Experimental results on real data show that the prediction method based on IR loss can provide more stable and consistent prediction result when test samples are generated from imprecision range.
SpecGrav -- Detection of Gravitational Waves using Deep Learning
Authors: Hrithika Dodia, Himanshu Tandel, Lynette D'Mello
Date: 2021-07-08 00:00:00
Tasks: Astronomy, Deep Learning, GPU

Gravitational waves are ripples in the fabric of space-time that travel at the speed of light. The detection of gravitational waves by LIGO is a major breakthrough in the field of astronomy. Deep Learning has revolutionized many industries including health care, finance and education. Deep Learning techniques have also been explored for detection of gravitational waves to overcome the drawbacks of traditional matched filtering method. However, in several researches, the training phase of neural network is very time consuming and hardware devices with large memory are required for the task. In order to reduce the extensive amount of hardware resources and time required in training a neural network for detecting gravitational waves, we made SpecGrav. We use 2D Convolutional Neural Network and spectrograms of gravitational waves embedded in noise to detect gravitational waves from binary black hole merger and binary neutron star merger. The training phase of our neural network was of about just 19 minutes on a 2GB GPU.
Energy Efficient Federated Learning in Integrated Fog-Cloud Computing Enabled Internet-of-Things Networks
Authors: Mohammed S. Al-Abiad, Md. Zoheb Hassan, Md. Jahangir Hossain
Date: 2021-07-07 00:00:00
Tasks: Cloud Computing, Federated Learning, Scheduling

We investigate resource allocation scheme to reduce the energy consumption of federated learning (FL) in the integrated fog-cloud computing enabled Internet-of-things (IoT) networks. In the envisioned system, IoT devices are connected with the centralized cloud server (CS) via multiple fog access points (F-APs). We consider two different scenarios for training the local models. In the first scenario, local models are trained at the IoT devices and the F-APs upload the local model parameters to the CS. In the second scenario, local models are trained at the F-APs based on the collected data from the IoT devices and the F-APs collaborate with the CS for updating the model parameters. Our objective is to minimize the overall energy-consumption of both scenarios subject to FL time constraint. Towards this goal, we devise a joint optimization of scheduling of IoT devices with the F-APs, transmit power allocation, computation frequency allocation at the devices and F-APs and decouple it into two subproblems. In the first subproblem, we optimize the IoT device scheduling and power allocation, while in the second subproblem, we optimize the computation frequency allocation. For each scenario, we develop a conflict graph based solution to iteratively solve the two subproblems. Simulation results show that the proposed two schemes achieve a considerable performance gain in terms of the energy consumption minimization. The presented simulation results interestingly reveal that for a large number of IoT devices and large data sizes, it is more energy efficient to train the local models at the IoT devices instead of the F-APs.
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
Authors: Yusuke Tashiro, Jiaming Song, Yang song, Stefano Ermon
Date: 2021-07-07 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Audio Synthesis, Image Generation, Imputation, Missing Values, Time Series, Time Series Analysis

The imputation of missing values in time series has many applications in healthcare and finance. While autoregressive models are natural candidates for time series imputation, score-based diffusion models have recently outperformed existing counterparts including autoregressive models in many tasks such as image generation and audio synthesis, and would be promising for time series imputation. In this paper, we propose Conditional Score-based Diffusion models for Imputation (CSDI), a novel time series imputation method that utilizes score-based diffusion models conditioned on observed data. Unlike existing score-based approaches, the conditional diffusion model is explicitly trained for imputation and can exploit correlations between observed values. On healthcare and environmental data, CSDI improves by 40-65% over existing probabilistic imputation methods on popular performance metrics. In addition, deterministic imputation by CSDI reduces the error by 5-20% compared to the state-of-the-art deterministic imputation methods. Furthermore, CSDI can also be applied to time series interpolation and probabilistic forecasting, and is competitive with existing baselines. The code is available at https://github.com/ermongroup/CSDI.
Impossibility results for fair representations
Authors: Tosca Lechner, Shai Ben-David, Sushant Agarwal, Nivasini Ananthakrishnan
Date: 2021-07-07 00:00:00
Tasks: Fairness

With the growing awareness to fairness in machine learning and the realization of the central role that data representation has in data processing tasks, there is an obvious interest in notions of fair data representations. The goal of such representations is that a model trained on data under the representation (e.g., a classifier) will be guaranteed to respect some fairness constraints. Such representations are useful when they can be fixed for training models on various different tasks and also when they serve as data filtering between the raw data (known to the representation designer) and potentially malicious agents that use the data under the representation to learn predictive models and make decisions. A long list of recent research papers strive to provide tools for achieving these goals. However, we prove that this is basically a futile effort. Roughly stated, we prove that no representation can guarantee the fairness of classifiers for different tasks trained using it; even the basic goal of achieving label-independent Demographic Parity fairness fails once the marginal data distribution shifts. More refined notions of fairness, like Odds Equality, cannot be guaranteed by a representation that does not take into account the task specific labeling rule with respect to which such fairness will be evaluated (even if the marginal data distribution is known a priory). Furthermore, except for trivial cases, no representation can guarantee Odds Equality fairness for any two different tasks, while allowing accurate label predictions for both. While some of our conclusions are intuitive, we formulate (and prove) crisp statements of such impossibilities, often contrasting impressions conveyed by many recent works on fair representations.
Differentiable Random Access Memory using Lattices
Authors: Adam P. Goucher, Rajan Troll
Date: 2021-07-07 00:00:00
Tasks: Language Modelling

We introduce a differentiable random access memory module with $O(1)$ performance regardless of size, scaling to billions of entries. The design stores entries on points of a chosen lattice to calculate nearest neighbours of arbitrary points efficiently by exploiting symmetries. Augmenting a standard neural network architecture with a single memory layer based on this, we can scale the parameter count up to memory limits with negligible computational overhead, giving better accuracy at similar cost. On large language modelling tasks, these enhanced models with larger capacity significantly outperform the unmodified transformer baseline. We found continued scaling with memory size up to the limits tested.
Model Selection for Generic Contextual Bandits
Authors: Avishek Ghosh, Abishek Sankararaman, Kannan Ramchandran
Date: 2021-07-07 00:00:00
Tasks: model, Model Selection, Multi-Armed Bandits

We consider the problem of model selection for the general stochastic contextual bandits under the realizability assumption. We propose a successive refinement based algorithm called Adaptive Contextual Bandit ({\ttfamily ACB}), that works in phases and successively eliminates model classes that are too simple to fit the given instance. We prove that this algorithm is adaptive, i.e., the regret rate order-wise matches that of any provable contextual bandit algorithm (ex. \cite{falcon}), that needs the knowledge of the true model class. The price of not knowing the correct model class turns out to be only an additive term contributing to the second order term in the regret bound. This cost possess the intuitive property that it becomes smaller as the model class becomes easier to identify, and vice-versa. We also show that a much simpler explore-then-commit (ETC) style algorithm also obtains similar regret bound, despite not knowing the true model class. However, the cost of model selection is higher in ETC as opposed to in {\ttfamily ACB}, as expected. Furthermore, for the special case of linear contextual bandits, we propose specialized algorithms that obtain sharper guarantees compared to the generic setup.