Search Results for author:
Found 21 papers, 8 papers with code
cs@DravidianLangTech-EACL2021: Offensive Language Identification Based On Multilingual BERT Model
Based on the features of the data set code mixture, we use multilingual BERT and TextCNN for semantic extraction and text classification.
cs_english@LT-EDI-EACL2021: Hope Speech Detection Based On Fine-tuning ALBERT Model
This paper mainly introduces the relevant content of the task “Hope Speech Detection for Equality, Diversity, and Inclusion at LT-EDI 2021-EACL 2021”.
Learning Harmonic Molecular Representations on Riemannian Manifold
In this work, we propose a Harmonic Molecular Representation learning (HMR) framework, which represents a molecule using the Laplace-Beltrami eigenfunctions of its molecular surface.
Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning
They have yet to develop the capability to address novel objects or spurious biases in real-world scenarios, and also fall short of interpreting the rationales behind their decisions.
Learning From Unique Perspectives: User-Aware Saliency Modeling
Our work aims to advance attention research from three distinct perspectives: (1) We present a new model with the flexibility to capture attention patterns of various combinations of users, so that we can adaptively predict personalized attention, user group attention, and general saliency at the same time with one single model; (2) To augment models with knowledge about the composition of attention from different users, we further propose a principled learning method to understand visual attention in a progressive manner; and (3) We carry out extensive analyses on publicly available saliency datasets to shed light on the roles of visual preferences.
No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution
Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms' SCM strategies.
NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29. 00dB on DIV2K validation set.
Attention in Reasoning: Dataset, Analysis, and Modeling
In this work, we propose an Attention with Reasoning capability (AiR) framework that uses attention to understand and improve the process leading to task outcomes.
REX: Reasoning-aware and Grounded Explanation
Finally, with our new data and method, we perform extensive analyses to study the effectiveness of our explanation under different settings, including multi-task learning and transfer learning.
VisualHow: Multimodal Problem Solving
With an overarching goal of developing intelligent systems to assist humans in various daily activities, we propose VisualHow, a free-form and open-ended research that focuses on understanding a real-life problem and deriving its solution by incorporating key components across multiple modalities.
Cross-Task Consistency Learning Framework for Multi-Task Learning
We theoretically prove that both losses help the model learn more efficiently and that cross-task consistency loss is better in terms of alignment with the straight-forward predictions.
On the Global Convergence of Gradient Descent for multi-layer ResNets in the mean-field regime
Finding the optimal configuration of parameters in ResNet is a nonconvex minimization problem, but first-order methods nevertheless find the global optimum in the overparameterized regime.
Overparameterization of deep ResNet: zero loss and mean-field analysis
Finding parameters in a deep neural network (NN) that fit training data is a nonconvex optimization problem, but a basic first-order optimization method (gradient descent) finds a global optimizer with perfect fit (zero-loss) in many practical situations.
Simulation on the Transparency of Electrons and Ion Back Flow for a Time Projection Chamber based on Staggered Multiple THGEMs
The IBF and the transparent rate of electrons are two essential indicators of TPC, which affect the energy resolution and counting rate respectively.
Instrumentation and Detectors High Energy Physics - Experiment
A first econometric analysis of the CRIX family
Here we provide a first econometric analysis of the CRIX family within a time-series framework.
Regularization Approach for Network Modeling of German Power Derivative Market
The network analysis of the full German power derivative market helps us to complement a full picture of system risk, and have a better understanding of the German power market functioning and environment.
AiR: Attention with Reasoning Capability
In this work, we propose an Attention with Reasoning capability (AiR) framework that uses attention to understand and improve the process leading to task outcomes.
Triaging moderate COVID-19 and other viral pneumonias from routine blood tests
The COVID-19 is sweeping the world with deadly consequences.
Classical limit for the varying-mass Schrödinger equation with random inhomogeneities
The varying-mass Schr\"odinger equation (VMSE) has been successfully applied to model electronic properties of semiconductor hetero-structures, for example, quantum dots and quantum wells.
Numerical Analysis Numerical Analysis
Attention-Based Autism Spectrum Disorder Screening With Privileged Modality
Photo-taking reveals attentional preference of subjects, differentiating people with ASD from healthy people, and is also easy to implement in real-world clinical settings without requiring advanced diagnostic instruments.
Boosted Attention: Leveraging Human Attention for Image Captioning
Visual attention has shown usefulness in image captioning, with the goal of enabling a caption model to selectively focus on regions of interest.
