141 citations · 209 across the 14 of their papers we have counts for
15 papers
Influence of Organic Spacer Cation on Dark Excitons in 2D Perovskites
Christopher G. Bailey, Lara V. Gillan, Minwoo Lee +5
The organic spacer cation plays a crucial role in determining the exciton fine structure in two-dimensional (2D) perovskites. Here, we use low-temperature magneto-optical spectrosc…
Target-Agnostic Gender-Aware Contrastive Learning for Mitigating Bias in Multilingual Machine Translation
Minwoo Lee, Hyukhun Koh, Kang-il Lee +3
Gender bias is a significant issue in machine translation, leading to ongoing research efforts in developing bias mitigation techniques. However, most works focus on debiasing bili…
Asking Clarification Questions to Handle Ambiguity in Open-Domain QA
Dongryeol Lee, Segwang Kim, Minwoo Lee +4
Ambiguous questions persist in open-domain question answering, because formulating a precise question with a unique answer is often challenging. Previously, Min et al. (2020) have…
Error-related Potential Variability: Exploring the Effects on Classification and Transferability
Benjamin Poole, Minwoo Lee
Brain-Computer Interfaces (BCI) have allowed for direct communication from the brain to external applications for the automatic detection of cognitive processes such as error recog…
GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition
Ekkasit Pinyoanuntapong, Ayman Ali, Kalvik Jakkala +5
mmWave radar-based gait recognition is a novel user identification method that captures human gait biometrics from mmWave radar return signals. This technology offers privacy prote…
GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer
Ekkasit Pinyoanuntapong, Ayman Ali, Pu Wang +2
Most existing gait recognition methods are appearance-based, which rely on the silhouettes extracted from the video data of human walking activities. The less-investigated skeleton…
Privacy Enhancement for Cloud-Based Few-Shot Learning
Archit Parnami, Muhammad Usama, Liyue Fan +1
Requiring less data for accurate models, few-shot learning has shown robustness and generality in many application domains. However, deploying few-shot models in untrusted environm…
Learning from Few Examples: A Summary of Approaches to Few-Shot Learning
Archit Parnami, Minwoo Lee
Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Requiring a large number of data samples, many deep learnin…
Towards Interactive Reinforcement Learning with Intrinsic Feedback
Benjamin Poole, Minwoo Lee
Reinforcement learning (RL) and brain-computer interfaces (BCI) have experienced significant growth over the past decade. With rising interest in human-in-the-loop (HITL), incorpor…
Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks
Archit Parnami, Mayuri Deshpande, Anant Kumar Mishra +1
Recent advances in neural networks have solved common graph problems such as link prediction, node classification, node clustering, node recommendation by developing embeddings of…
Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning
Matias Mendieta, Taojiannan Yang, Pu Wang +3
Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distributi…
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge
Pinyarash Pinyoanuntapong, Prabhu Janakaraj, Ravikumar Balakrishnan +3
Federated learning (FL) is a distributed machine learning technology for next-generation AI systems that allows a number of workers, i.e., edge devices, collaboratively learn a sha…
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing
Pinyarash Pinyoanuntapong, Tagore Pothuneedi, Ravikumar Balakrishnan +3
Federated Learning (FL) over wireless multi-hop edge computing networks, i.e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm. This paper presents F…
Demystifying Deep Neural Networks Through Interpretation: A Survey
Giang Dao, Minwoo Lee
Modern deep learning algorithms tend to optimize an objective metric, such as minimize a cross entropy loss on a training dataset, to be able to learn. The problem is that the sing…
Few-Shot Keyword Spotting With Prototypical Networks
Archit Parnami, Minwoo Lee
Recognizing a particular command or a keyword, keyword spotting has been widely used in many voice interfaces such as Amazon's Alexa and Google Home. In order to recognize a set of…