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Woonhyuk Baek

Publications and source records attributed to Woonhyuk Baek.

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Role of $p$-$d$ Hybridization on Optical Properties of Chalcopyrite Semiconductors

Designing quantum materials for coherent optical properties is a central agenda in quantum technology. Semiconductor quantum dots are an emerging approach for controlling coherent optical properties via confinement effects, tunable band gaps, and exciton binding energies, yet their inherent structural and compositional inhomogeneity degrades the coherence of the optical spectra, posing a major obstacle. We show that, for chalcopyrite semiconductors, hybridization between transition-metal $d$ and ligand $p$ electrons in the valence band is key to the coherence of the quantum dot optical spectrum. We demonstrate this using first-principles electronic-structure calculations and optical spectroscopy. The strong $p$-$d$ hybridization in CuInS$_{2}$ induces the Cu($d$) Coulomb scattering channel, giving rise to the incoherent photodoped hole carrier, while the weak $p$-$d$ hybridization in AgInS$_{2}$ induces the delocalized photodoped hole carrier having a predominant S($p$) orbital character. Our experimental results on optical spectra suggest that when the Cu ratio is enhanced in the Ag$_{1-x}$Cu$_{x}$In$_{1-y}$Ga$_{y}$S$_{2}$ quantum dot, Cu atoms at both Ag sites and defect sites experience enhanced $p$-$d$ hybridization, and a coupling begins to develop between the electrons in the quantum dot and the defect electrons at a small Cu ratio. This coupling activates Cu($d$) Coulomb scattering for photodoped holes traversing the defect sites, producing an incoherent optical response that naturally explains the long-standing absence of band-edge spectral signatures in CuIn$_{1-y}$Ga$_y$S$_2$ quantum dots. These results serve as a guideline for designing semiconductor quantum dots. To achieve a coherent optical spectrum, avoid $p$-$d$-hybridized orbital character in the photo-doped carrier.

cond-mat.mtrl-sci

AutoCLINT: The Winning Method in AutoCV Challenge 2019

NeurIPS 2019 AutoDL challenge is a series of six automated machine learning competitions. Particularly, AutoCV challenges mainly focused on classification tasks on visual domain. In this paper, we introduce the winning method in the competition, AutoCLINT. The proposed method implements an autonomous training strategy, including efficient code optimization, and applies an automated data augmentation to achieve the fast adaptation of pretrained networks. We implement a light version of Fast AutoAugment to search for data augmentation policies efficiently for the arbitrarily given image domains. We also empirically analyze the components of the proposed method and provide ablation studies focusing on AutoCV datasets.

cs.LG

torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models

We design and implement a ready-to-use library in PyTorch for performing micro-batch pipeline parallelism with checkpointing proposed by GPipe (Huang et al., 2019). In particular, we develop a set of design components to enable pipeline-parallel gradient computation in PyTorch's define-by-run and eager execution environment. We show that each component is necessary to fully benefit from pipeline parallelism in such environment, and demonstrate the efficiency of the library by applying it to various network architectures including AmoebaNet-D and U-Net. Our library is available at https://github.com/kakaobrain/torchgpipe .

cs.DC

Spatially Attentive Output Layer for Image Classification

Most convolutional neural networks (CNNs) for image classification use a global average pooling (GAP) followed by a fully-connected (FC) layer for output logits. However, this spatial aggregation procedure inherently restricts the utilization of location-specific information at the output layer, although this spatial information can be beneficial for classification. In this paper, we propose a novel spatial output layer on top of the existing convolutional feature maps to explicitly exploit the location-specific output information. In specific, given the spatial feature maps, we replace the previous GAP-FC layer with a spatially attentive output layer (SAOL) by employing a attention mask on spatial logits. The proposed location-specific attention selectively aggregates spatial logits within a target region, which leads to not only the performance improvement but also spatially interpretable outputs. Moreover, the proposed SAOL also permits to fully exploit location-specific self-supervision as well as self-distillation to enhance the generalization ability during training. The proposed SAOL with self-supervision and self-distillation can be easily plugged into existing CNNs. Experimental results on various classification tasks with representative architectures show consistent performance improvements by SAOL at almost the same computational cost.

cs.CV

Scalable Neural Architecture Search for 3D Medical Image Segmentation

In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space. Our NAS framework searches the structure of each layer including neural connectivities and operation types in both of the encoder and decoder. Since optimizing over a large discrete architecture space is difficult due to high-resolution 3D medical images, a novel stochastic sampling algorithm based on a continuous relaxation is also proposed for scalable gradient based optimization. On the 3D medical image segmentation tasks with a benchmark dataset, an automatically designed architecture by the proposed NAS framework outperforms the human-designed 3D U-Net, and moreover this optimized architecture is well suited to be transferred for different tasks.

cs.LG