Efficient end-to-end learning for quantizable representations

Embedding representation learning via neural networks is at the core foundation of modern similarity based search. While much effort has been put in developing algorithms for learning binary hamming code representations for search efficiency, this still requires a linear scan of the entire dataset per each query and trades off the search accuracy through binarization. To this end, we consider the problem of directly learning a quantizable embedding representation and the sparse binary hash code end-to-end which can be used to construct an efficient hash table not only providing significant search reduction in the number of data but also achieving the state of the art search accuracy outperforming previous state of the art deep metric learning methods. We also show that finding the optimal sparse binary hash code in a mini-batch can be computed exactly in polynomial time by solving a minimum cost flow problem. Our results on Cifar-100 and on ImageNet datasets show the state of the art search accuracy in precision@k and NMI metrics while providing up to 98X and 478X search speedup respectively over exhaustive linear search. The source code is available at https://github.com/maestrojeong/Deep-Hash-Table-ICML18Comment: Accepted and to appear at ICML 2018. Camera ready versio

EMI: Exploration with Mutual Information

Reinforcement learning algorithms struggle when the reward signal is very sparse. In these cases, naive random exploration methods essentially rely on a random walk to stumble onto a rewarding state. Recent works utilize intrinsic motivation to guide the exploration via generative models, predictive forward models, or discriminative modeling of novelty. We propose EMI, which is an exploration method that constructs embedding representation of states and actions that does not rely on generative decoding of the full observation but extracts predictive signals that can be used to guide exploration based on forward prediction in the representation space. Our experiments show competitive results on challenging locomotion tasks with continuous control and on image-based exploration tasks with discrete actions on Atari. The source code is available at https://github.com/snu-mllab/EMI .Comment: Accepted and to appear at ICML 201

Improvement in carrier mobility of metal oxide thin-film transistor by a microstructure modification

Metal oxide thin-film transistors (TFTs) have been rapidly penetrating as an emerging backplane technology for the next generation high pixel density, large-size liquid crystal displays and organic light-emitting diodes panels because of their intriguing properties such as their high field-effect mobility, low subthreshold gate swing, good uniformity, low temperature processing capability, and transparency to visible light.[1-3] However, the typical field-effect mobility of IGZO TFTs in the practical production line is ~10 cm2/Vs, which is still not enough to drive the high-end flat panel displays with the ultra-high-definition, large size ( 60 inch) and high frame rate ( 240 Hz). One of ways to improve the mobility of electron carriers in metal oxide semiconductor would involve the lattice ordering, which leads to the substantial reduction in the carrier scattering with the semiconductor. Approach that seeks to utilize the crystallization of metal oxide semiconductor has yet to be attempted despite the potential scientific and engineering implication. In this presentation, we explored the metal-induced crystallization of amorphous zinc thin oxide (a-ZTO) and indium gallium zinc oxide (a-IGZO) semiconductor at a low temperature. The fabricated crystalline ZTO TFTs exhibited a high field-effect mobility of 33.5 cm2/Vs, subthreshold gate swing of 0.40 V/decade, and ION/OFF ratio of \u3e 5 107. The method in this study is expected to be applied to any type of metal oxide semiconductor. Acknowledgment This study was supported by the National Research Foundation of Korea (NRF) grant funded the Korean government (NRF-2015R1A2A2A01003848) and the industrial strategic technology development program funded by MKE/KEIT (10051403). References 1. K. Nomura et al., Nature 432, 488 (2004). 2. T. Kamyia et al., Sci. Technol. Adv. Mater. 11, 044305 (2010). 3. J. Y. Kwon and J. K. Jeong, Semicond. Sci. Technol. 30, 024002 (2015

Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic Programming

Recent works on neural network pruning advocate that reducing the depth of the network is more effective in reducing run-time memory usage and accelerating inference latency than reducing the width of the network through channel pruning. In this regard, some recent works propose depth compression algorithms that merge convolution layers. However, the existing algorithms have a constricted search space and rely on human-engineered heuristics. In this paper, we propose a novel depth compression algorithm which targets general convolution operations. We propose a subset selection problem that replaces inefficient activation layers with identity functions and optimally merges consecutive convolution operations into shallow equivalent convolution operations for efficient end-to-end inference latency. Since the proposed subset selection problem is NP-hard, we formulate a surrogate optimization problem that can be solved exactly via two-stage dynamic programming within a few seconds. We evaluate our methods and baselines by TensorRT for a fair inference latency comparison. Our method outperforms the baseline method with higher accuracy and faster inference speed in MobileNetV2 on the ImageNet dataset. Specifically, we achieve $1.41\times$ speed-up with $0.11$\%p accuracy gain in MobileNetV2-1.0 on the ImageNet.Comment: ICML 2023; Codes at https://github.com/snu-mllab/Efficient-CNN-Depth-Compressio

Clinical Significance of Glycolytic Metabolic Activity in Hepatocellular Carcinoma

High metabolic activity is a hallmark of cancers, including hepatocellular carcinoma (HCC). However, the molecular features of HCC with high metabolic activity contributing to clinical outcomes and the therapeutic implications of these characteristics are poorly understood. We aimed to define the features of HCC with high metabolic activity and uncover its association with response to current therapies. By integrating gene expression data from mouse liver tissues and tumor tissues from HCC patients (n = 1038), we uncovered three metabolically distinct HCC subtypes that differ in clinical outcomes and underlying molecular biology. The high metabolic subtype is characterized by poor survival, the strongest stem cell signature, high genomic instability, activation of EPCAM and SALL4, and low potential for benefitting from immunotherapy. Interestingly, immune cell analysis showed that regulatory T cells (Tregs) are highly enriched in high metabolic HCC tumors, suggesting that high metabolic activity of cancer cells may trigger activation or infiltration of Tregs, leading to cancer cells\u27 evasion of anti-cancer immune cells. In summary, we identified clinically and metabolically distinct subtypes of HCC, potential biomarkers associated with these subtypes, and a potential mechanism of metabolism-mediated immune evasion by HCC cells

Review of Machine Learning Applications Using Retinal Fundus Images

Automating screening and diagnosis in the medical field saves time and reduces the chances of misdiagnosis while saving on labor and cost for physicians. With the feasibility and development of deep learning methods, machines are now able to interpret complex features in medical data, which leads to rapid advancements in automation. Such efforts have been made in ophthalmology to analyze retinal images and build frameworks based on analysis for the identification of retinopathy and the assessment of its severity. This paper reviews recent state-of-the-art works utilizing the color fundus image taken from one of the imaging modalities used in ophthalmology. Specifically, the deep learning methods of automated screening and diagnosis for diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma are investigated. In addition, the machine learning techniques applied to the retinal vasculature extraction from the fundus image are covered. The challenges in developing these systems are also discussed

Learning discrete and continuous factors of data via alternating disentanglement

© 36th International Conference on Machine Learning, ICML 2019. All rights reserved.We address the problem of unsupervised disentanglement of discrete and continuous explanatory factors of data. We first show a simple procedure for minimizing the total correlation of the continuous latent variables without having to use a discriminator network or perform importance sampling, via cascading the information flow in the β-vae framework. Furthermore, we propose a method which avoids offloading the entire burden of jointly modeling the continuous and discrete factors to the variational encoder by employing a separate discrete inference procedure. This leads to an interesting alternating minimization problem which switches between finding the most likely discrete configuration given the continuous factors and updating the variational encoder based on the computed discrete factors. Experiments show that the proposed method clearly disentangles discrete factors and significantly outperforms current disentanglement methods based on the disentanglement score and inference network classification score. The source code is available at https://github.com/snumllab/DisentanglementICML 19.N