Robust Learning via Golden Symmetric Loss of (un)Trusted Labels

Learning robust deep models against noisy labels becomes ever critical when today's data is commonly collected from open platforms and subject to adversarial corruption. The information on the label corruption process, i.e., corruption matrix, can greatly enhance the robustness of deep models but still fall behind in combating hard classes....

TrustNet: Learning from Trusted Data Against (A)symmetric Label Noise

Big Data systems allow collecting massive datasets to feed the data hungry deep learning. Labelling these ever-bigger datasets is increasingly challenging and label errors affect even highly curated sets. This makes robustness to label noise a critical property for weakly-supervised classifiers. The related works on resilient deep networks...

Focus is Key to Success: A Focal Loss Function for Deep Learning-Based Side-Channel Analysis

The deep learning-based side-channel analysis represents one of the most powerful side-channel attack approaches. Thanks to its capability in dealing with raw features and countermeasures, it becomes the de facto standard approach for the SCA community. The recent works significantly improved the deep learning-based attacks from various...

First-Principles Calculation of Optoelectronic Properties in 2D Materials: The Polytypic WS₂ Case

The phenomenon of polytypism, namely unconventional crystal phases displaying a mixture of stacking sequences, represents a powerful handle to design and engineer novel physical properties in two-dimensional (2D) materials. In this work, we characterize from first-principles the optoelectronic properties associated with the 2H/3R polytypism...

Financial option valuation by unsupervised learning with artificial neural networks

Artificial neural networks (ANNs) have recently also been applied to solve partial differential equations (PDEs). The classical problem of pricing European and American financial options, based on the corresponding PDE formulations, is studied here. Instead of using numerical techniques based on finite element or difference methods, we...

Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting

Participation of wind energy in the generation portfolio of power systems is increasing, making it more challenging for system operators to adequately maintain system security. It therefore becomes increasingly crucial to accurately predict the wind generation. This work investigates how different parameters influence the performance of...

Individual yield nowcasting for residential PV systems

Due to the inherent uncertainty in photovoltaic (PV) energy generation, an accurate power forecasting is essential to ensure a reliable operation of PV systems and a safe electric grid. Machine learning (ML) techniques have gained popularity on the development of this task due to its increased accuracy. Most literature, however, focuses only...

No (good) loss no gain: Systematic evaluation of loss functions in deep learning-based side-channel analysis

Deep learning is a powerful direction for profiling side-channel analysis as it can break targets protected with countermeasures even with a relatively small number of attack traces. Still, it is necessary to conduct hyperparameter tuning to reach strong attack performance, which can be far from trivial. Besides many options stemming from the...

Classifying Continuous Labels: A Simple Tweak to Make Regression Robust

Regression is difficult because of noise, imbalanced data sampling, missing data, etc. We propose a method by classifying the continuous regression labels to tackle regression robustness problems. We analyze if our method can help regression, given that the class information is already included in the regression labels. We start by extensively...

Congestion forecasting using a custom loss function: Application to congestion mitigation on substation Middelharnis

Whereas in the past, the Distribution System Operator (DSO) almost never encountered congestion in their grids, nowadays, with the increase of connected renewable energy sources, this will become more prevalent. To forecast congestion on transformer stations, with the goal of mitigating it, the Dutch DSO Stedin uses machine learning models with...

Robotic Auxiliary Losses for continuous reinforcement learning

Recent advancements in computation power and artificial intelligence have allowed the creation of advanced reinforcement learning models which could revolutionize, between others, the field of robotics. As model and environment complexity increase, however, training solely through the feedback of environment reward becomes more difficult. From...

Sharpening the Future of Occupancy Grid Map Prediction Methods: An Investigation into Loss Functions and Semantic Segmentation Multi-Task learning for More Accurate OGM Predictions

For an Autonomous Vehicle (AV) to traverse safely in traffic, It is vital it can anticipate the behavior of surrounding traffic participants using motion prediction. Current motion prediction approaches can be categorized into object-centered and object-agnostic methods and are primarily based on deep learning. The former relies on a human...

Generating virtual CT from MRI using fully convolutional neural networks with improved structure quality

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Perceptual losses in precipitation nowcasting: Exploring limits and potential

Accurate short term rain predictions are important for flood early warning systems, emergency services, energy management and other services that that make weather dependent decisions. Recently introduced machine learning models suffer from blurry and unrealistic predictions at longer lead times, causing poor performance on the rarer heavy...

Loss functions for profiled side-channel analysis: An analysis of loss functions and the application of multi­-loss functions for deep learning in the SCA domain

Deep learning techniques have become the tool of choice for side-channel analysis. In recent years, neural networks like multi-layer perceptrons and convolutional neural networks have proven to be the most powerful instruments for performing side-channel analysis. Recent work on this topic has focused on different aspects of these techniques,...