pwc-archive/papers-with-abstracts huggingface dataset:
How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?
Authors: Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Xinchao Wang, Jiashi Feng
Date: 2021-09-28 00:00:00
Proceeding: NeurIPS Workshop ImageNet_PPF 2021 12
Tasks: Self-Supervised Learning

Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained. Unfortunately, such a problem setting is often impractical if not infeasible since many real-world tasks rely on sequential learning, e.g., data are decentralized or collected in a streaming fashion. In this paper, we conduct the first thorough and dedicated investigation on self-supervised pre-training with streaming data, aiming to shed light on the model behavior under this overlooked setup. Specifically, we pre-train over 500 models on four categories of pre-training streaming data from ImageNet and DomainNet and evaluate them on three types of downstream tasks and 12 different downstream datasets. Our studies show that, somehow beyond our expectation, with simple data replay or parameter regularization, sequential self-supervised pre-training turns out to be an efficient alternative for joint pre-training, as the performances of the former are mostly on par with those of the latter. Moreover, catastrophic forgetting, a common issue in sequential supervised learning, is much alleviated in sequential self-supervised learning (SSL), which is well justified through our comprehensive empirical analysis on representations and the sharpness of minima in the loss landscape. Our findings, therefore, suggest that, in practice, for SSL, the cumbersome joint training can be replaced mainly by sequential learning, which in turn enables a much broader spectrum of potential application scenarios.
ImageNet suffers from dichotomous data difficulty
Authors: Kristof Meding, Luca M. Schulze Buschoff, Robert Geirhos, Felix A. Wichmann
Date: 2021-09-28 00:00:00
Proceeding: NeurIPS Workshop ImageNet_PPF 2021 12
Tasks: Inductive Bias

"The power of a generalization system follows directly from its biases" (Mitchell 1980). Today, CNNs are incredibly powerful generalisation systems---but to what degree have we understood how their inductive bias influences model decisions? We here attempt to disentangle the various aspects that determine how a model decides. In particular, we ask: what makes one model decide differently from another? In a meticulously controlled setting, we find that (1.) irrespective of the network architecture or objective (e.g. self-supervised, semi-supervised, vision transformers, recurrent models) all models end up with a similar decision boundary. (2.) To understand these findings, we analysed model decisions on the ImageNet validation set from epoch to epoch and image by image. We find that the ImageNet validation set suffers from dichotomous data difficulty (DDD): For the range of investigated models and their accuracies, it is dominated by 46.3% "trivial" and 11.3% "impossible" images. Only 42.4%  of the images are responsible for the differences between two models' decision boundaries. The impossible images are not driven by label errors. (3.) Finally, humans are highly accurate at predicting which images are "trivial" and "impossible" for CNNs (81.4%). Taken together, it appears that ImageNet suffers from dichotomous data difficulty. This implies that in future comparisons of brains, machines and behaviour, much may be gained from investigating the decisive role of images and the distribution of their difficulties.
Transfer learning with fewer ImageNet classes
Authors: Michal Kucer, Diane Oyen
Date: 2021-09-28 00:00:00
Proceeding: NeurIPS Workshop ImageNet_PPF 2021 12
Tasks: Transfer Learning

  Though much previous work tried to uncover the best practices for transfer learning, much is left unexplored. Our preliminary work explores the effect of removing a portion of the ImageNet classes with low per-class validation accuracy on the accuracy of the remaining classes. Furthermore, we explore if models trained with a reduced number of classes are suitable for transfer learning.
Curating the Twitter Election Integrity Datasets for Better Online Troll Characterization
Authors: Albert Manuel Orozco Camacho, Reihaneh Rabbany
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12

In modern days, social media platforms provide accessible channels for the inter-action and immediate reflection of the most important events happening around the world. In this paper, we, firstly, present a curated set of datasets whose origin stem from the Twitter’s Information Operations efforts.  More notably, these accounts, which have been already suspended, provide a notion of how state-backed human trolls operate.Secondly, we present detailed analyses of how these behaviours vary over time,and motivate its use and abstraction in the context of deep representation learning:for instance, to learn and, potentially track, troll behaviour. We present baselinesf or such tasks and highlight the differences there may exist within the literature.Finally, we utilize the representations learned for behaviour prediction to classify trolls from"real"users, using a sample of non-suspended active accounts.
Lifting the veil on hyper-parameters for value-based deep reinforcement learning
Authors: João Guilherme Madeira Araújo, Johan Samir Obando Ceron, Pablo Samuel Castro
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Successful applications of deep reinforcement learning (deep RL) combine algorithmic design and careful hyper-parameter selection. The former often comes from iterative improvements over existing algorithms, while the latter is either inherited from prior methods or tuned for the specific method being introduced. Although critical to a method’s performance, the effect of the various hyper-parameter choices are often overlooked in favour of algorithmic advances. In this paper, we perform an initial empirical investigation into a number of often-overlooked hyper-parameters for value-based deep RL agents, demonstrating their varying levels of importance. We conduct this study on a varied set of classic control environments which helps highlight the effect each environment has on an algorithm’s hyper-parameter sensitivity.
Flexible Learning of Sparse Neural Networks via Constrained $L_0$ Regularization
Authors: Jose Gallego-Posada, Juan Ramirez De Los Rios, Akram Erraqabi
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12

We propose to approach the problem of learning $L_0$-sparse networks using a constrained formulation of the optimization problem. This is in contrast to commonly used penalized approaches, which combine the regularization terms additively with the (surrogate) empirical risk. Our experiments demonstrate that we can obtain approximate solutions to the constrained optimization problem with comparable performance to state-of-the art methods for $L_0$-sparse training. Finally, we discuss how this constrained approach provides greater (hyper-)parameter interpretability and accountability from a practitioner's point of view.
Vehicle Speed Estimation Using Computer Vision And Evolutionary Camera Calibration
Authors: Hector Mejia, Esteban Palomo, Ezequiel López-Rubio, Israel Pineda, Rigoberto Fonseca
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12
Tasks: Camera Calibration, Homography Estimation, Vehicle Speed Estimation

Currently, the standard for vehicle speed estimation is radar or lidar speed signs which can be costly to buy and maintain. However, most major cities already implement networks of traffic surveillance cameras that can be utilized for vehicle speed estimation using computer vision. This work implements such a system using homography estimation, YOLOv4 object detector, and an object tracker capable of vehicle speed estimation. The homography component uses world plane-image plane point correspondences, located by humans. Moreover, a new method is developed specifically for this use case, using the estimation of density evolutionary algorithm. It aims at correcting the points misalignment in between planes. In addition, a basic direct linear transformation (DLT) and a random sample consensus robust version of DLT are implemented for comparison. Finally, the results show that the proposed homography method reduces the projection error from world to image point by 97\%, when compared to the other two methods, and the complete workflow can successfully estimate speed distributions expected from vehicles on urban traffic and handle dynamic changes in vehicle speed.
Performance Analysis of Quantum Machine Learning Classifiers
Authors: Tonni Jui, Olawale Ayoade, Pablo Rivas, Javier Orduz
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12
Tasks: BIG-bench Machine Learning, Quantum Machine Learning

In recent years, researchers have started looking into data transformations in quantum computation. They want to see how quantum computing affects the robustness and performance of machine learning methods. Quantum mechanics succeed in explaining some phenomena where classical formulas failed in the past. Thus, it expanded in analytical research fields such as Quantum Machine Learning (QML) over the years. The developing QML discipline has proven solutions to issues that are equivalent (or comparable) to those addressed by classical machine learning, including classification and prediction problems using quantum classifiers. As a result of these factors, quantum classifier analysis has become one of the most important topics in QML. This paper studies four quantum classifiers: Support Vector Classification with Quantum Kernel (SVCQK), Quantum Support Vector Classifier (QSVC), Variational Quantum Classifier (VQC), and Circuit Quantum Neural Network Classifier (CQNNC). We also report case study outcomes and results analysis utilizing linearly and non-linearly separable datasets generated. Our research is to explore if quantum information may aid learning or convergence.
Investigating generative neural-network models for building pest insect detectors in sticky trap images for the Peruvian horticulture
Authors: Joel Jossue Cabrera Rios, Edwin Villanueva
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12

Pest insects are a problem in horticulture, so early detection is key for their control. Sticky traps are an inexpensive way to obtain insect samples in crops, but identifying them manually is a time-consuming task. Building computational models to identify insect species in sticky trap images is therefore highly desirable. However, this is a challenging task due to the difficulty in getting sizeable sets of training images. In this paper, we studied the usefulness of three neural network generative models to synthesize pest insect images (DCGAN, WGAN, and VAE) for augmenting the training set and thus facilitate the induction of insect detector models. Experiments with images of seven species of pest insects of the Peruvian horticulture showed that the WGAN and VAE models are able to learn to generate images of such species. It was also found that the synthesized images can help to induce YOLOv5 detectors with significant gains in detection performance compared to not using synthesized data. A demo app that integrates the detector models can be accessed through the URL: https://bit.ly/3uXW0Ee. Project repository is available at: https://github.com/weirdfish23/pest-insects-GAN
A Pharmacovigilance Application of Social Media Mining: An Ensemble Approach for Automated Classification and Extraction of Drug Mentions in Tweets
Authors: Luis Alberto Robles Hernandez, Rajath Chikkatur Srinivasa, Juan M Banda
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Pharmacovigilance

Researchers have extensively used social media platforms like Twitter for knowledge discovery purposes, as tweets are considered a wealth of information that provides unique insights. Recent developments have further enabled social media mining for various biomedical tasks such as pharmacovigilance. A first step towards identifying a use-case of Twitter for the pharmacovigilance domain is to extract medication/drug terminologies mentioned in the tweets, which is a challenging task due to several reasons. For example, drug mentions in tweets may be incorrectly written, making the identification of these mentions more difficult. In this work, we propose a two step approach, first, we focused on classifying tweets with drug mentions via an ensemble model (containing transformer models), second, we extract drug mentions (along with their span positions) using a text-tagging/dictionary based approach, and a Named Entity Recognition (NER) approach. By comparing these two entity identification approaches, we demonstrate that using only a dictionary-based approach is not enough.
A novel stochastic model based on echo state networks for hydrological time series forecasting
Authors: edson luque mamani
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12
Tasks: Management, Network Embedding, Time Series, Time Series Analysis, Time Series Forecasting

The Stochastic Streamflow Models (SSMS) are time series models for precise prediction of hydrological data useful in hydrologic risk management. Nowadays, deep learning networks get many considerations in time series forecasting. However, despite their theoretical benefits, they fail due to their drawbacks, such as complex architectures, slow convergence and the vanishing gradient problem. In order to alleviate these drawbacks, we propose a new stochastic model applied in problems that involve stochastic behavior and periodic characteristics. The new model has two components, the first one, a type of recurrent neural network embedding the echo-state (ESN) learning mechanism instead of conventional backpropagation. The last component adds the uncertainty associated with stationary processes. This model is called Stochastic Streamflow Model ESN (SSMESN). It was calibrated with time series of monthly discharge data from MOPEX data set. Preliminar results show that the SSMESN can achieve a significant prediction performance, learning speed. This model, can be considered a first attempt that applies the echo state network methodology to stochastic process.
Do You See What I See: Using Augmented Reality and Artificial Intelligence
Authors: Anonymous
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12
Tasks: Keypoint Estimation, Pose Estimation

Do You See What I See: Using Augmented Reality and Artificial Intelligence 

We explore the challenges of real-world applications of augmented reality (AR) and artificial intelligence (AI) through experiments that demonstrate interactions with an augmented world. We interrogate applications of AR and AI, their limitations, and social impacts amplified by the pandemic. We share code, explore ethical considerations, and future projects. 

We conduct three experiments to apply the theory of pose estimation with deep learning [1][2][3][4]. In our first experiment, we implement AR using segmentation to augment the scene captured by laptop webcam. In the second experiment, we use keypoint estimation using a deep neural network for pose estimation. In the last experiment, we implement pose estimation against different backgrounds. These experiments enable us to reflect on how context matters.[5] We ask, who is at the table when applications are built and deployed, and invite you too to reflect on the challenges associated with using AR and AI and our responsibilities as builders as tech.
Towards EEG signals codification using contrastiveloss
Authors: Anonymous
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12
Tasks: Contrastive Learning, EEG, Electroencephalogram (EEG)

In this work we explore the use of contrastive learning to obtain vectorial representation of EEG signals. As contrastive learning is a self-supervised method, we propose the use of consecutive segments up to a certain window of time to define a property that will guide the contrastive learning. The results are promising given the small sample of EEG signals that we have access.
On the Pitfalls of Label Differential Privacy
Authors: Andres Munoz Medina, Robert Istvan Busa-Fekete, Umar Syed, Sergei Vassilvitskii
Date: 2021-10-16 00:00:00
Proceeding: NeurIPS Workshop LatinX_in_AI 2021 12

We study the privacy limitations of label differential privacy, which has emerged as an intermediate trust model between local and central differential privacy, where only the label of each training example is protected (and the features are assumed to be public). We show that the guarantees provided by label DP are significantly weaker than they appear, as an adversary can "un-noise" the perturbed labels. Formally we show that the privacy loss has a close connection with Jeffreys' divergence of the conditional distribution between positive and negative labels, which allows explicit formulation of the trade-off between utility and privacy in this setting. Our results suggest how to select public features that optimize this trade-off. But we still show that there is no free lunch---instances where label differential privacy guarantees are strong are exactly those where a good classifier does not exist. We complement the negative results with a non-parametric estimator for the true privacy loss, and apply our techniques on large-scale benchmark data to demonstrate how to
achieve a desired privacy protection.
Dual Architecture for Name Entity Extraction and Relation Extraction with Applications in Medical Corpora
Authors: Anonymous
Date: 2021-10-16 00:00:00
Proceeding: ACL ARR November 2021 11
Tasks: Relation, Relation Extraction

There is a growing interest in automatic knowledge discovery in plain text documents. Automation enables the analysis of massive collections of information. Such efforts are especially relevant in the health domain as advancements could use the large volume of available resources to transform areas important for society when addressing various health research challenges. However, knowledge discovery is usually aided by annotated corpora, which are scarce resources in the literature. This situation is particularly critical in the Spanish language, for which the volume of training resources is less widespread. This work considers as a start point existent health-oriented Spanish dataset. In addition, it also creates an English variant using the same tagging system. Furthermore, we design and analyze two separated architectures for Entity Extraction and Relation Recognition that outperform previous works in the Spanish dataset. With such promising results, we also evaluate their performance in the English version.
Deciding What's Fair: Challenges of Applying Reinforcement Learning in Online Marketplaces
Authors: Anonymous
Date: 2021-10-12 00:00:00
Proceeding: NeurIPS Workshop PERLS 2021 12
Tasks: Fairness, reinforcement-learning, Reinforcement Learning (RL)

Reinforcement learning (RL) techniques offer a versatile and powerful extension to the toolkit for computer scientists and marketplace designers for their use in online marketplaces. As the use of these techniques continues to expand, their application in online marketplaces raise questions of their appropriate use, particularly around issues of fairness and market transparency. I argue that the use of RL techniques, alongside similar calls in domains such as automated vehicle systems, is a problem of sociotechnical specification that faces a set of normative and regulatory challenges unique to marketplaces. I provide a selective overview of the RL literature as applied to markets to illustrate challenges associated with the use of RL techniques in online marketplaces. I conclude with a discussion of capacity-building in research and institutions that is required in order for benefits from algorithmically managed marketplaces to be realized for stakeholders and broader society.
Power and Accountability in RL-driven Environmental Policy
Authors: Anonymous
Date: 2021-10-12 00:00:00
Proceeding: NeurIPS Workshop PERLS 2021 12
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning (RL)

Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques available to address pressing environmental problems (e.g., climate change, biodiversity loss), Reinforcement Learning (RL) may both hold the greatest promise and present the most pressing perils. This paper explores how RL-driven policy refracts existing power relations in the environmental domain while also creating unique challenges to ensuring equitable and accountable environmental decision processes. We focus on how RL technologies shift the distribution of decision-making, agenda-setting, and ideological power between resource users, governing bodies, and private industry.
Aligning AI with Security and Democracy Perceptions to Improve QoE and QoL
Authors: Anonymous
Date: 2021-10-12 00:00:00
Proceeding: NeurIPS Workshop PERLS 2021 12

Rapid advancements in artificial intelligence (AI) along with the deployments of more data-driven pervasive technologies are currently offering incredible possibilities and solving problems in innovative ways. The pace of AI's development is so fast that it is hard to follow in details all the ethical and social implications. Therefore, it is legitimate to assess the effect of AI-based technologies on day to day quality of life. This paper analyzes the effect of the user's Quality of Experience (QoE) when using these new AI-based services on the Quality of Life (QoL). In particular, this paper focuses on two aspects: security and democratization. The security threats of any new technology defines the QoE especially nowadays after revealing warning signs regarding privacy threats and lack of public trust. Furthermore, democratization perception in terms of technology used is also a crucial feature that governs steering AI development towards ethically serving the interests of the society; and therefore improving the QoE and QoL.  Hence, we propose a framework to provide comprehensive analysis of the current status in the existing literature while focusing on the anticipated tools for democratization. In addition, we highlight some research gaps and suggest future opportunities to be addressed.
Computational Learning in Economic Games: Competitive Effects of Pricing Software Systems
Authors: Anonymous
Date: 2021-10-12 00:00:00
Proceeding: NeurIPS Workshop PERLS 2021 12

The paper is not complete. We provide an extended abstract.
Demanding and Designing Aligned Cognitive Architectures
Authors: Anonymous
Date: 2021-10-12 00:00:00
Proceeding: NeurIPS Workshop PERLS 2021 12
Tasks: BIG-bench Machine Learning

With AI systems becoming more powerful and pervasive, there is increasing debate about keeping their actions aligned with the broader goals and needs of humanity.  This multi-disciplinary and multi-stakeholder debate must resolve many issues, here we examine two of them.  The first is to clarify what demands stakeholders might usefully make on the designers of AI systems, useful because the technology exists to implement them.  We introduce the framing of cognitive architectures to make this technical topic more accessible.  The second issue is how stakeholders should calibrate their interactions with modern machine learning researchers.  We consider how current fashions in machine learning create a narrative pull that participants in technical and policy discussions should be aware of, so that they can compensate for it.  We identify several technically tractable but currently unfashionable options for improving AI alignment.