pwc-archive/papers-with-abstracts huggingface dataset:
Machine Learning in Asset Management—Part 2: Portfolio Construction—Weight Optimization. The Journal of Financial Data Science
Authors: Dr Derek Snow
Date: 2020-03-26 00:00:00
Proceeding: Journal of Financial Data Science 2020 3
Tasks: Articles, Asset Management, BIG-bench Machine Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This is the second in a series of articles dealing with machine learning in asset management. This article focuses on portfolio weighting using machine learning. Following from the previous article (Snow 2020), which looked at trading strategies, this article identifies different weight optimization methods for supervised, unsupervised, and reinforcement learning frameworks. In total, seven submethods are summarized with the code made available for further exploration.
DeltaPy: A Framework for Tabular Data Augmentation in Python
Authors: Derek Snow
Date: 2020-05-22 00:00:00
Proceeding: The Alan Turing Institute 2020 5
Tasks: BIG-bench Machine Learning, Data Augmentation, Feature Engineering, Synthetic Data Generation

A range of data abstractions have come to the fore since the re-emergence of machine learning. This includes procedures like feature engineering, extraction, transformation, and selection, as well as data pre-processing, generation, synthesisation, and augmentation. This report attempts to unify some of this terminology with the development of a bare-bones Python package, DeltaPy.
MTSS-GAN: Multivariate Time Series Simulation Generative Adversarial Networks
Authors: Derek Snow
Date: 2020-06-26 00:00:00
Proceeding: The Alan Turing Institute 2020 6
Tasks: Generative Adversarial Network, Image Generation, Synthetic Data Generation, Time Series, Time Series Analysis, Time Series Generation

MTSS-GAN is a new generative adversarial network (GAN) developed to simulate diverse multivariate time series (MTS) data with finance applications in mind. The purpose of this synthesiser is two-fold, we both want to generate data that accurately represents the original data, while also having the flexibility to generate data with novel and unique relationships that could help with model testing and robustness checks. The method is inspired by stacked GANs originally designed for image generation. Stacked GANs have produced some of the best quality images, for that reason MTSS-GAN is expected to be a leading contender in multivariate time series generation.
AtsPy: Automated Time Series Forecasting in Python
Authors: Derek Snow
Date: 2020-05-14 00:00:00
Proceeding: The Alan Turing Institute 2020 5
Tasks: Survey, Time Series, Time Series Analysis, Time Series Forecasting

This short report deals with the recent rise of programmatic time series methods. This decade has witnessed the proliferation of commercial and open source time-series tooling, which calls for an exposition of what is publicly available. In tandem with this survey, AtsPy, an open source automated time series framework is developed as a working prototype to showcase the ability of state of the art univariate time series methods.
FairPut: A Light Framework for Machine Learning Fairness with LightGBM
Authors: Derek Snow
Date: 2020-10-22 00:00:00
Proceeding: The Alan Turing Institute 2020 10
Tasks: BIG-bench Machine Learning, Fairness

This is a holistic framework to approach fair prediction outputs at the individual and group level. This framework includes quantitative monotonic measures, residual explanations, benchmark competition, adversarial attacks, disparate error analysis, model agnostic pre-and post-processing, reasoning codes, counterfactuals, contrastive explanations, and prototypical examples. A number novel techniques are proposed in this framework, each of which could benefit from future examination.
High-order compact finite difference scheme for option pricing in stochastic volatility jump models
Authors: Bertram Düring, Alexander Pitkin
Date: 2017-04-18 00:00:00

We derive a new high-order compact finite difference scheme for option pricing in stochastic volatility jump models, e.g. in Bates model. In such models the option price is determined as the solution of a partial integro-differential equation. The scheme is fourth order accurate in space and second order accurate in time. Numerical experiments for the European option pricing problem are presented. We validate the stability of the scheme numerically and compare its performance to standard finite difference and finite element methods. The new scheme outperforms a standard discretisation based on a second-order central finite difference approximation in all our experiments. At the same time, it is very efficient, requiring only one initial $LU$-factorisation of a sparse matrix to perform the option price valuation. Compared to finite element approaches, it is very parsimonious in terms of memory requirements and computational effort, since it achieves high-order convergence without requiring additional unknowns, unlike finite element methods with higher polynomial order basis functions. The new high-order compact scheme can also be useful to upgrade existing implementations based on standard finite differences in a straightforward manner to obtain a highly efficient option pricing code.
A Surprising Thing: The Application of Machine Learning Ensembles and Signal Theory to Predict Earnings Surprises
Authors: Derek Snow
Date: 2017-07-27 00:00:00
Proceeding: PhD Thesis 2017 7
Tasks: Feature Engineering

Nonlinear classification models can predict future earnings surprises with a high accuracy by using pricing and earnings input data. Surprises of 15% or more can be predicted with 71% accuracy. These predictions can be used to form profitable trading strategies. Additional variables have been created using signal-processing and handcrafted feature-engineering methods. Some of these variables have in the past been known to be related to analyst bias. The machine learning model in effect corrects for analyst mistakes and biases by incorporating these variables into a nonlinear prediction model to predict future earnings surprises.
DataGene: A Framework for Dataset Similarity
Authors: Derek Snow
Date: 2020-06-05 00:00:00
Proceeding: The Alan Turing Institute 2020 6

DataGene is developed to identify data set similarity between real and synthetic datasets as well as train, test, and validation datasets. For many modelling and software development tasks there is a need for datasets to have share similar characteristics. This has traditionally been achieved with visualizations, DataGene seeks to replace these visual methods with a range of novel quantitative methods. Please see the GitHub repository to inspect and install the Python code.
PandaPy: A Wrapper Around Structured Arrays to Mimic ‘Structs’ in the C Language
Authors: Derek Snow
Date: 2020-05-13 00:00:00
Proceeding: The Alan Turing Institute 2020 5

Similar to the original Pandas project, PandaPy is developed to improve the usability of python for finance. Structured data types are designed to be able to mimic ‘structs’ in the C language, and they share a similar memory layout. The biggest benefit of this approach is that NumPy directly maps onto a C structure definition, so the buffer containing the array content can be accessed directly within an appropriately written C program. This makes PandaPy a strong contender for high frequency trading on small-to-medium datasets. PandaPy currently houses more than 30 functions.
TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer
Authors: Berkay Berabi, Jingxuan He, Veselin Raychev, Martin Vechev
Date: 2021-07-18 00:00:00
Proceeding: ICML 2021 7
Tasks: Code Generation, Multi-Task Learning, Program Repair, Transfer Learning

The problem of fixing errors in programs has attracted substantial interest over the years. The key challenge for building an effective code fixing tool is to capture a wide range of errors and meanwhile maintain high accuracy. In this paper, we address this challenge and present a new learning-based system, called TFix. TFix works directly on program text and phrases the problem of code fixing as a text-to-text task. In turn, this enables it to leverage a powerful Transformer based model pre-trained on natural language and fine-tuned to generate code fixes (via a large, high-quality dataset obtained from GitHub commits). TFix is not specific to a particular programming language or class of defects and, in fact, improved its precision by simultaneously fine-tuning on 52 different error types reported by a popular static analyzer. Our evaluation on a massive dataset of JavaScript programs shows that TFix is practically effective: it is able to synthesize code that fixes the error in  67 percent of cases and significantly outperforms existing learning-based approaches.
COCO Attributes: Attributes for People, Animals, and Objects
Authors: Genevieve Patterson, James Hays
Date: 2016-09-17 00:00:00
Proceeding: ECCV 2016 9
Tasks: Attribute, Object

In this paper, we discover and annotate visual attributes for the COCO dataset. With the goal of enabling deeper object understanding, we deliver the largest attribute dataset to date. Using our COCO Attributes dataset, a fine-tuned classification system can do more than recognize object categories -- for example, rendering multi-label classifications such as ''sleeping spotted curled-up cat'' instead of simply ''cat''. To overcome the expense of annotating thousands of COCO object instances with hundreds of attributes, we present an Economic Labeling Algorithm (ELA) which intelligently generates crowd labeling tasks based on correlations between attributes. The ELA offers a substantial reduction in labeling cost while largely maintaining attribute density and variety. Currently, we have collected 3.5 million object-attribute pair annotations describing 180 thousand different objects. We demonstrate that our efficiently labeled training data can be used to produce classifiers of similar discriminative ability as classifiers created using exhaustively labeled ground truth. Finally, we provide baseline performance analysis for object attribute recognition.
Wave-Informed Matrix Factorization with Global Optimality Guarantees
Authors: Harsha Vardhan Tetali, Joel B. Harley, Benjamin D. Haeffele
Date: 2021-07-19 00:00:00
Tasks: Dictionary Learning, Representation Learning, Structural Health Monitoring

With the recent success of representation learning methods, which includes deep learning as a special case, there has been considerable interest in developing representation learning techniques that can incorporate known physical constraints into the learned representation. As one example, in many applications that involve a signal propagating through physical media (e.g., optics, acoustics, fluid dynamics, etc), it is known that the dynamics of the signal must satisfy constraints imposed by the wave equation. Here we propose a matrix factorization technique that decomposes such signals into a sum of components, where each component is regularized to ensure that it satisfies wave equation constraints. Although our proposed formulation is non-convex, we prove that our model can be efficiently solved to global optimality in polynomial time. We demonstrate the benefits of our work by applications in structural health monitoring, where prior work has attempted to solve this problem using sparse dictionary learning approaches that do not come with any theoretical guarantees regarding convergence to global optimality and employ heuristics to capture desired physical constraints.
Compressing Multisets with Large Alphabets using Bits-Back Coding
Authors: Daniel Severo, James Townsend, Ashish Khisti, Alireza Makhzani, Karen Ullrich
Date: 2021-07-15 00:00:00

Current methods which compress multisets at an optimal rate have computational complexity that scales linearly with alphabet size, making them too slow to be practical in many real-world settings. We show how to convert a compression algorithm for sequences into one for multisets, in exchange for an additional complexity term that is quasi-linear in sequence length. This allows us to compress multisets of exchangeable symbols at an optimal rate, with computational complexity decoupled from the alphabet size. The key insight is to avoid encoding the multiset directly, and instead compress a proxy sequence, using a technique called `bits-back coding'. We demonstrate the method experimentally on tasks which are intractable with previous optimal-rate methods: compression of multisets of images and JavaScript Object Notation (JSON) files. Code for our experiments is available at https://github.com/facebookresearch/multiset-compression.
Learning a Joint Embedding of Multiple Satellite Sensors: A Case Study for Lake Ice Monitoring
Authors: Manu Tom, Yuchang Jiang, Emmanuel Baltsavias, Konrad Schindler
Date: 2021-07-19 00:00:00
Tasks: Earth Observation, Lake Ice Monitoring, Representation Learning, Sensor Fusion

Fusing satellite imagery acquired with different sensors has been a long-standing challenge of Earth observation, particularly across different modalities such as optical and Synthetic Aperture Radar (SAR) images. Here, we explore the joint analysis of imagery from different sensors in the light of representation learning: we propose to learn a joint embedding of multiple satellite sensors within a deep neural network. Our application problem is the monitoring of lake ice on Alpine lakes. To reach the temporal resolution requirement of the Swiss Global Climate Observing System (GCOS) office, we combine three image sources: Sentinel-1 SAR (S1-SAR), Terra MODIS, and Suomi-NPP VIIRS. The large gaps between the optical and SAR domains and between the sensor resolutions make this a challenging instance of the sensor fusion problem. Our approach can be classified as a late fusion that is learned in a data-driven manner. The proposed network architecture has separate encoding branches for each image sensor, which feed into a single latent embedding. I.e., a common feature representation shared by all inputs, such that subsequent processing steps deliver comparable output irrespective of which sort of input image was used. By fusing satellite data, we map lake ice at a temporal resolution of < 1.5 days. The network produces spatially explicit lake ice maps with pixel-wise accuracies > 91% (respectively, mIoU scores > 60%) and generalises well across different lakes and winters. Moreover, it sets a new state-of-the-art for determining the important ice-on and ice-off dates for the target lakes, in many cases meeting the GCOS requirement.
Accelerating deep neural networks for efficient scene understanding in automotive cyber-physical systems
Authors: Stavros Nousias, Erion-Vasilis Pikoulis, Christos Mavrokefalidis, Aris S. Lalos
Date: 2021-07-19 00:00:00
Tasks: Model Compression, object-detection, Object Detection, Scene Understanding

Automotive Cyber-Physical Systems (ACPS) have attracted a significant amount of interest in the past few decades, while one of the most critical operations in these systems is the perception of the environment. Deep learning and, especially, the use of Deep Neural Networks (DNNs) provides impressive results in analyzing and understanding complex and dynamic scenes from visual data. The prediction horizons for those perception systems are very short and inference must often be performed in real time, stressing the need of transforming the original large pre-trained networks into new smaller models, by utilizing Model Compression and Acceleration (MCA) techniques. Our goal in this work is to investigate best practices for appropriately applying novel weight sharing techniques, optimizing the available variables and the training procedures towards the significant acceleration of widely adopted DNNs. Extensive evaluation studies carried out using various state-of-the-art DNN models in object detection and tracking experiments, provide details about the type of errors that manifest after the application of weight sharing techniques, resulting in significant acceleration gains with negligible accuracy losses.
Separating Skills and Concepts for Novel Visual Question Answering
Authors: Spencer Whitehead, Hui Wu, Heng Ji, Rogerio Feris, Kate Saenko
Date: 2021-07-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Attribute, Contrastive Learning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. To measure generalization to novel questions, we propose to separate them into "skills" and "concepts". "Skills" are visual tasks, such as counting or attribute recognition, and are applied to "concepts" mentioned in the question, such as objects and people. VQA methods should be able to compose skills and concepts in novel ways, regardless of whether the specific composition has been seen in training, yet we demonstrate that existing models have much to improve upon towards handling new compositions. We present a novel method for learning to compose skills and concepts that separates these two factors implicitly within a model by learning grounded concept representations and disentangling the encoding of skills from that of concepts. We enforce these properties with a novel contrastive learning procedure that does not rely on external annotations and can be learned from unlabeled image-question pairs. Experiments demonstrate the effectiveness of our approach for improving compositional and grounding performance.
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition
Authors: Benjamin Spetter-Goldstein, Nataniel Ruiz, Sarah Adel Bargal
Date: 2021-07-19 00:00:00
Proceeding: ICML Workshop AML 2021 7
Tasks: Adversarial Attack, Face Recognition

The modern open internet contains billions of public images of human faces across the web, especially on social media websites used by half the world's population. In this context, Face Recognition (FR) systems have the potential to match faces to specific names and identities, creating glaring privacy concerns. Adversarial attacks are a promising way to grant users privacy from FR systems by disrupting their capability to recognize faces. Yet, such attacks can be perceptible to human observers, especially under the more challenging black-box threat model. In the literature, the justification for the imperceptibility of such attacks hinges on bounding metrics such as $\ell_p$ norms. However, there is not much research on how these norms match up with human perception. Through examining and measuring both the effectiveness of recent black-box attacks in the face recognition setting and their corresponding human perceptibility through survey data, we demonstrate the trade-offs in perceptibility that occur as attacks become more aggressive. We also show how the $\ell_2$ norm and other metrics do not correlate with human perceptibility in a linear fashion, thus making these norms suboptimal at measuring adversarial attack perceptibility.
DeepSocNav: Social Navigation by Imitating Human Behaviors
Authors: Juan Pablo de Vicente, Alvaro Soto
Date: 2021-07-19 00:00:00
Tasks: Social Navigation, Unity

Current datasets to train social behaviors are usually borrowed from surveillance applications that capture visual data from a bird's-eye perspective. This leaves aside precious relationships and visual cues that could be captured through a first-person view of a scene. In this work, we propose a strategy to exploit the power of current game engines, such as Unity, to transform pre-existing bird's-eye view datasets into a first-person view, in particular, a depth view. Using this strategy, we are able to generate large volumes of synthetic data that can be used to pre-train a social navigation model. To test our ideas, we present DeepSocNav, a deep learning based model that takes advantage of the proposed approach to generate synthetic data. Furthermore, DeepSocNav includes a self-supervised strategy that is included as an auxiliary task. This consists of predicting the next depth frame that the agent will face. Our experiments show the benefits of the proposed model that is able to outperform relevant baselines in terms of social navigation scores.
A Comparison of Supervised and Unsupervised Deep Learning Methods for Anomaly Detection in Images
Authors: Vincent Wilmet, Sauraj Verma, Tabea Redl, Håkon Sandaker, Zhenning Li
Date: 2021-07-20 00:00:00
Tasks: Anomaly Detection, Supervised Anomaly Detection

Anomaly detection in images plays a significant role for many applications across all industries, such as disease diagnosis in healthcare or quality assurance in manufacturing. Manual inspection of images, when extended over a monotonously repetitive period of time is very time consuming and can lead to anomalies being overlooked.Artificial neural networks have proven themselves very successful on simple, repetitive tasks, in some cases even outperforming humans. Therefore, in this paper we investigate different methods of deep learning, including supervised and unsupervised learning, for anomaly detection applied to a quality assurance use case. We utilize the MVTec anomaly dataset and develop three different models, a CNN for supervised anomaly detection, KD-CAE for autoencoder anomaly detection, NI-CAE for noise induced anomaly detection and a DCGAN for generating reconstructed images. By experiments, we found that KD-CAE performs better on the anomaly datasets compared to CNN and NI-CAE, with NI-CAE performing the best on the Transistor dataset. We also implemented a DCGAN for the creation of new training data but due to computational limitation and lack of extrapolating the mechanics of AnoGAN, we restricted ourselves just to the generation of GAN based images. We conclude that unsupervised methods are more powerful for anomaly detection in images, especially in a setting where only a small amount of anomalous data is available, or the data is unlabeled.
Understanding Gender and Racial Disparities in Image Recognition Models
Authors: Rohan Mahadev, Anindya Chakravarti
Date: 2021-07-20 00:00:00
Tasks: Fairness, image-classification, Image Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Large scale image classification models trained on top of popular datasets such as Imagenet have shown to have a distributional skew which leads to disparities in prediction accuracies across different subsections of population demographics. A lot of approaches have been made to solve for this distributional skew using methods that alter the model pre, post and during training. We investigate one such approach - which uses a multi-label softmax loss with cross-entropy as the loss function instead of a binary cross-entropy on a multi-label classification problem on the Inclusive Images dataset which is a subset of the OpenImages V6 dataset. We use the MR2 dataset, which contains images of people with self-identified gender and race attributes to evaluate the fairness in the model outcomes and try to interpret the mistakes by looking at model activations and suggest possible fixes.