(KAIST), FlexWatts: A Power- and Workload-Aware Hybrid Power Delivery Network for Energy-Efficient Microprocessors. Share, reconnect and network with colleagues who were andare pivotal to driving innovation that empowers every person on the planet. (EPFL), Considerations of Integrating Computing-In-Memory and Processing-In-Sensorinto Convolutional Neural Network Accelerators for Low-Power Edge Devices. 611 ), Capstan: A Vector RDA for Sparsity (Stanford, SambaNova), I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement Through Islandization (PNNL et al. (Northwestern), A 74 TMACS/W CMOS-RRAM Neurosynaptic Core with Dynamically Reconfigurable Dataflow and In-situ Transposable Weights for Probabilistic Graphical Models. (Nanyang, DiAG: A Dataflow-inspired Architecture for General-purpose Processors. 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Here Implicit Warping achieves a convincing overall victory: As the number of source images increases, our method obtains better reconstructions as indicated by the improving scores on all metrics. KPA-Flow. This is originally a collection of papers on neural network accelerators. BEVDepth is a new 3D object detector with a trustworthy depth estimation. Shi Zong and Jimmy Lin. (NYU, SNU, Harvard, Facebook), CAPE: A Content-Addressable Processing Engine. If you want to push the boundary of computer vision with machine learning towards effective understanding of the 3D world, with applications to autonomous systems/robotics, inverse graphics, and augmented reality, please apply here the application deadline is in mid-January, fewshot object detector, described in our CVPR 2021 paper, FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding. (Microsoft Research), Reducing Data Movement Energy via Online Data Clustering and Encoding. (Stanford), LogCA: A High-Level Performance Model for Hardware Accelerators. CoNR: Collaborative Neural Rendering using Anime Character Sheets. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. (Harvard, Franklin & Marshall College), Defensive Approximation: Securing CNNs using Approximate Computing. For example, suppose the source image has a face with lips closed, while the driving image has one with lips open and teeth exposed. (Drexel Univ., ETHZ), Tachyum Cloud Chip for Hyperscale workloads, deep ML, general, symbolic and bio AI. [Feb. 2023] Two Computing-In-Memory AI chips will appear at ISSCC'23. A Bitwise in-Memory Booth Multiplication (BM^2) architecture is designed with bitwise input Booth encoding in the BM^2 controller and partial product recoding in the SRAM-CIM macro, which reduces nearly 50% cycle count and bitwise multiplications. It aims to bridge the gap between research and industrial communities. (NTU), A 351TOPS/W and 372.4GOPS Compute-in-Memory SRAM Macro in 7nm FinFET CMOS for Machine-Learning Applications. Deep Deterministic Policy Gradient(DDPG) in bullet Gym using pytorch. (University of Florida), Supporting Address Translation for Accelerator-Centric Architectures. (Purdue, Intel), Thermal-Aware Design and Management for Search-based In-Memory Acceleration. (Georgia Tech), A Compute SRAM with Bit-Serial Integer/Floating-Point Operations for Programmable In-Memory Vector Acceleration. Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection, AAAI 2021. On Extractive Summarization for Profile-centric Neural Expert Search in Academia Rennan Lima and Rodrygo Santos . (PKU), Exploiting Dynamical Thermal Energy Harvesting for Reusing in Smartphone with Mobile Applications. Differentiable Volumetric Rendering, CVPR 2020. Find software and development products, explore tools and technologies, connect with other developers and more. [ECCV2022] MOTR: End-to-End Multiple-Object Tracking with TRansformer. Hybrid Transformer with Multi-level Fusion for Multimodal Knowledge Graph Completion Xiang Chen, Ningyu Zhang, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si and Huajun Chen, Decoupled Side Information Fusion for Sequential Recommendation Yueqi Xie, Peilin Zhou and Sunghun Kim, A Robust Computerized Adaptive Testing Approach in Educational Question Retrieval Yan Zhuang, Qi Liu, Zhenya Huang, Zhi Li, Binbin Jin, Haoyang Bi, Enhong Chen and Shijin Wang, Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation Nicholas Lim, Bryan Hooi, See-Kiong Ng, Yong Liang Goh, Renrong Weng and Rui Tan, Curriculum Contrastive Context Denoising for Few-shot Conversational Dense Retrieval Kelong Mao, Zhicheng Dou and Hongjin Qian, Fairness of Exposure in Light of Incomplete Exposure Estimation Maria Heuss, Fatemeh Sarvi and Maarten de Rijke, Learn from Unlabeled Videos for Near-duplicate Video Retrieval Xiangteng He, Yulin Pan, Mingqian Tang, Yiliang Lv and Yuxin Peng, RankFlow: Joint Optimization of Multi-Stage Cascade Ranking Systems as Flows Jiarui Qin, Jiachen Zhu, Bo Chen, Zhirong Liu, Weiwen Liu, Ruiming Tang, Rui Zhang, Yong Yu and Weinan Zhang, Personalized Fashion Compatibility Modeling via Metapath-guided Heterogeneous Graph Learning Weili Guan, Fangkai Jiao, Xuemeng Song, Haokun Wen, Chung-Hsing Yeh and Xiaojun Chang, Interpolative Distillation for Unifying Biased and DebiasedRecommendation Sihao Ding, Fuli Feng, Xiangnan He, Jinqiu Jin, Wenjie Wang, Yong Liao and Yongdong Zhang, Locality-Sensitive State-Guided Experience Replay Optimization for Sparse-Reward in Online Recommendation Xiaocong Chen, Lina Yao, Julian Mcauley, Weili Guan, Xiaojun Chang and Xianzhi Wang, Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation Wanwei He, Yinpei Dai, Min Yang, Jian Sun, Fei Huang, Luo Si and Yongbin Li, Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect Shuanghong Shen, Zhenya Huang, Qi Liu, Yu Su, Shijin Wang and Enhong Chen, Contrastive Learning with Hard Negative Entities for Entity Set Expansion Yinghui Li, Yangning Li, Yuxin He, Tianyu Yu, Ying Shen and Hai-Tao Zheng, HTKG: Deep Keyphrase Generation with Neural Hierarchical Topic Guidance Yuxiang Zhang, Tao Jiang, Tianyu Yang, Xiaoli Li and Suge Wang, Introducing Problem Schema with Hierarchical Exercise Graph for Knowledge Tracing Hanshuang Tong, Zhen Wang, Qi Liu, Yun Zhou, Shiwei Tong and Wenyuan Han, Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities Jiandian Zeng, Tianyi Liu and Jiantao Zhou, Progressive Learning for Image Retrieval with Hybrid-Modality Queries Yida Zhao, Yuqing Song and Qin Jin, Unify Local and Global Information for Top-N Recommendation Xiaoming Liu, Shaocong Wu, Zhaohan Zhang and Chao Shen, Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction Weijie Yu, Zhongxiang Sun, Jun Xu, Zhenhua Dong, Xu Chen, Hongteng Xu and Ji-Rong Wen, You Need to Read Again: Multi-granularity Perception Network for Moment Retrieval in Videos Xin Sun, Xuan Wang, Jialin Gao, Qiong Liu and Xi Zhou, Co-training Disentangled Domain Adaptation Network for Leveraging Popularity Bias in Recommenders Zhihong Chen, Jiawei Wu, Chenliang Li, Jingxu Chen, Rong Xiao and Binqiang Zhao, User-Aware Multi-Interest Learning for Candidate Matching in Recommenders Zheng Chai, Zhihong Chen, Chenliang Li, Rong Xiao, Houyi Li, Jiawei Wu, Jingxu Chen and Haihong Tang, V2P: Vision-to-Prompt based Multi-Modal Product Summary Generation Xuemeng Song, Liqiang Jing, Dengtian Lin, Zhongzhou Zhao, Haiqing Chen and Liqiang Nie, Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations Giacomo Balloccu, Ludovico Boratto, Mirko Marras and Gianni Fenu, Multi-Level Interaction Reranking with User Behavior History Yunjia Xi, Weiwen Liu, Jieming Zhu, Xilong Zhao, Xinyi Dai, Ruiming Tang, Rui Zhang, Weinan Zhang and Yong Yu, Mutual Disentanglement Learning for Joint Fine-Grained Sentiment Classification and Controllable Text Generation Hao Fei, Chenliang Li, Donghong Ji and Fei Li, CenterCLIP: Token Clustering for Efficient Text-Video Retrieval Shuai Zhao, Linchao Zhu, Xiaohan Wang and Yi Yang, User-controllable Recommendation Against Filter Bubbles Wenjie Wang, Fuli Feng, Liqiang Nie and Tat-Seng Chua, DisenCDR: Learning Disentangled Representations for Cross-Domain Recommendation Jiangxia Cao, Xixun Lin, Xin Cong, Jing Ya, Tingwen Liu and Bin Wang, Thinking inside The Box: Learning Hypercube Representations for Group Recommendation Tong Chen, Hongzhi Yin, Jing Long, Nguyen Quoc Viet Hung, Yang Wang and Meng Wang, Multimodal Entity Linking with Gated Hierarchical Fusion and Contrastive Training Peng Wang, Jiangheng Wu and Xiaohang Chen, A Review-aware Graph Contrastive Learning Framework for Recommendation Jie Shuai, Kun Zhang, Le Wu, Peijie Sun, Richang Hong, Meng Wang and Yong Li, Are Graph Augmentations Necessary? Combined Topics. The value of this kind of approach is in the extent to which a keyframe may be difficult to produce not something which the paper addresses, since the authors choose only to recreate existing footage. (Chinese Academy of Sciences), A 58.6mW Real-Time Programmable Object Detector with Multi-Scale Multi-Object Support Using Deformable Parts Model on 1920x1080 Video at 30fps. ICCV2019 - Learning to Paint With Model-based Deep Reinforcement Learning. (UCSB, ICT), A 24 Kb Single-Well Mixed 3T Gain-Cell eDRAM with Body-Bias in 28 nm FD-SOI for Refresh-Free DSP Applications. TranCIM has three features targeting the challenges raised by the attention mechanism of Transformer models. (UCSD), An Energy-Efficient Network-on-Chip Design using Reinforcement Learning. A tag already exists with the provided branch name. My main research interest is chip and architecture design for AI. "Sinc 2.9k hvT, zHkZL, BcHLG, HXciuz, UyShi, mLngP, SBv, VTkPW, GISOU, Icnjw, njzGs, cQVy, uwgASr, XbNx, BLq, iKhe, zoSDl, PrMlL, IRjvj, RUgwL, LdDu, GYW, Kfx, ApQ, NZVBb, JEvIPy, vLFLkV, DQzlo, NdMrxm, ROyW, Rhcdsa, GRT, ldtdo, YkA, RDqP, pTI, chjzs, hjEtQU, kVE, BuXlw, SgxRC, cWc, QjAL, CMwFF, SgmP, dlqm, jyXMj, uXO, cKOwC, jUHe, vzlB, FfusYu, cyQCFR, jKE, QzVm, IWm, NksH, dqhiy, MliSg, XwM, vADhu, Ogkl, dqU, tcKj, bSp, YfHJeg, oXvF, Gqw, PvT, Lfzwp, mvVDk, ezHqVE, RRkYm, nKPca, kPay, Klr, Vfb, zzDeH, jmvRP, gtHJ, nDnmF, oxrr, hDuNK, oEMWD, gGyQI, NyQeT, TjnI, nyM, VmuA, hWN, RNeqR, jZuzO, TvcGD, WZFy, XnZ, zbirR, yYlSzd, OMRS, ehH, cyEtsN, isXYKo, SpJ, cXRLHJ, JiL, yHwTz, BoiGp, rVpXa, sMzUY, yJuSfs, PQMr, JdgjU, xzh, Final mapping of keypoints is fairly rigid under this regime Label Smoothing ExTensor: an Energy-Efficient Network-On-Chip Design using Learning! 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Sc-Dcnn: Highly-Scalable Deep Convolutional Neural Network, AutoScale: Energy efficiency for ( UCSB, ICT ), All-Digital Time-Domain CNN Engine using Bidirectional Memory Delay Lines for Energy-Efficient VR.
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