Search Results for author:
Found 16 papers, 3 papers with code
FiNLP at FinCausal 2020 Task 1: Mixture of BERTs for Causal Sentence Identification in Financial Texts
This paper describes our system developed for the sub-task 1 of the FinCausal shared task in the FNP-FNS workshop held in conjunction with COLING-2020.
Accelerated Smoothing: A Scalable Approach to Randomized Smoothing
Randomized smoothing has emerged as a potent certifiable defense against adversarial attacks by employing smoothing noises from specific distributions to ensure the robustness of a smoothed classifier.
Trust, But Verify: A Survey of Randomized Smoothing Techniques
A paradigm shift from empirical defences to certification-based defences has been observed in response.
Rapid Training Data Creation by Synthesizing Medical Images for Classification and Localization
We show the efficacy of this approach on both a weakly supervised localization model and a strongly supervised localization model.
[Re] Double Sampling Randomized Smoothing
This paper is a contribution to the reproducibility challenge in the field of machine learning, specifically addressing the issue of certifying the robustness of neural networks (NNs) against adversarial perturbations.
A Chance-Constrained Optimal Design of Volt/VAR Control Rules for Distributed Energy Resources
Deciding setpoints for distributed energy resources (DERs) via local control rules rather than centralized optimization offers significant autonomy.
How human-derived brain organoids are built differently from brain organoids derived of genetically-close relatives: A multi-scale hypothesis
We postulate that the enhancement of ZEB2 expression driving this intermediate state is potentially due to chromatin reorganization.
Scalable Optimal Design of Incremental Volt/VAR Control using Deep Neural Networks
According to non-incremental control rules, such as the one mandated by the IEEE Standard 1547, the reactive power setpoint of each DER is computed as a piecewise-linear curve of the local voltage.
Optimal Design of Volt/VAR Control Rules of Inverters using Deep Learning
This task of optimal rule design (ORD) is challenging as Volt/VAR rules introduce nonlinear dynamics, and lurk trade-offs between stability and steady-state voltage profiles.
Optimal Design of Volt/VAR Control Rules for Inverter-Interfaced Distributed Energy Resources
The IEEE 1547 Standard for the interconnection of distributed energy resources (DERs) to distribution grids provisions that smart inverters could be implementing Volt/VAR control rules among other options.
Neural Implicit Surface Reconstruction from Noisy Camera Observations
Representing 3D objects and scenes with neural radiance fields has become very popular over the last years.
[Re] Distilling Knowledge via Knowledge Review
This effort aims to reproduce the results of experiments and analyze the robustness of the review framework for knowledge distillation introduced in the CVPR '21 paper 'Distilling Knowledge via Knowledge Review' by Chen et al.
DNN-based Policies for Stochastic AC OPF
Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty.
Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach
The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions.
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing
A major challenge in modern neural networks is the utilization of previous knowledge for new tasks in an effective manner, otherwise known as transfer learning.
Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset
In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed.
