Integrating structured biological data by Kernel Maximum Mean Discrepancy. - Karsten M. Borgwardt; Arthur Gretton; Malte J. Rasch; Hans-Peter Kriegel; Bernhard Schölkopf; Alexander J. Smola (ISMB (Supplement of Bioinformatics), 2006)

A Kernel Method for the Two-Sample-Problem. - Arthur Gretton; Karsten M. Borgwardt; Malte J. Rasch; Bernhard Schölkopf; Alexander J. Smola (NIPS, 2006)

A Kernel Approach to Comparing Distributions. - Arthur Gretton; Karsten M. Borgwardt; Malte J. Rasch; Bernhard Schölkopf; Alexander J. Smola (AAAI, 2007)

Learning Variance Statistics of Natural Images. - Libo Ma; Malte J. Rasch; Si Wu 001 (ISNN (2), 2011)

A Kernel Two-Sample Test. - Arthur Gretton; Karsten M. Borgwardt; Malte J. Rasch; Bernhard Schölkopf; Alexander J. Smola (J. Mach. Learn. Res., 2012)

Nonlinear multiplicative dendritic integration in neuron and network models. - Danke Zhang; Yuanqing Li 001; Malte J. Rasch; Si Wu 001 (Frontiers Comput. Neurosci., 2013)

Design principles of the sparse coding network and the role of "sister cells" in the olfactory system of Drosophila. - Danke Zhang; Yuanqing Li 001; Si Wu 001; Malte J. Rasch (Frontiers Comput. Neurosci., 2013)

A Phenomenological Synapse Model for Asynchronous Neurotransmitter Release. - Tao Wang 092; Luping Yin; Xiaolong Zou; Yousheng Shu; Malte J. Rasch; Si Wu 001 (Frontiers Comput. Neurosci., 2015)

Training LSTM Networks with Resistive Cross-Point Devices. - Tayfun Gokmen; Malte J. Rasch; Wilfried Haensch (CoRR, 2018)

Efficient ConvNets for Analog Arrays. - Malte J. Rasch; Tayfun Gokmen; Mattia Rigotti; Wilfried Haensch (CoRR, 2018)

Training Large-Scale Spiking Neural Networks on Multi-core Neuromorphic System Using Backpropagation. - Megumi Ito; Malte J. Rasch; Masatoshi Ishii; Atsuya Okazaki; SangBum Kim; Junka Okazawa; Akiyo Nomura; Kohji Hosokawa; Wilfried Haensch (ICONIP (3), 2019)

Training large-scale ANNs on simulated resistive crossbar arrays. - Malte J. Rasch; Tayfun Gokmen; Wilfried Haensch (CoRR, 2019)

Zero-shifting Technique for Deep Neural Network Training on Resistive Cross-point Arrays. - Hyungjun Kim; Malte J. Rasch; Tayfun Gokmen; Takashi Ando; Hiroyuki Miyazoe; Jae-Joon Kim; John Rozen; Seyoung Kim (CoRR, 2019)

Neural network accelerator design with resistive crossbars: Opportunities and challenges. - Shubham Jain; Aayush Ankit; Indranil Chakraborty; Tayfun Gokmen; Malte J. Rasch; Wilfried Haensch; Kaushik Roy 001; Anand Raghunathan (IBM J. Res. Dev., 2019)

Synchronized Analog Capacitor Arrays for Parallel Convolutional Neural Network Training. - Effendi Leobandung; Malte J. Rasch; Y. Li (MWSCAS, 2020)

Training Large-scale Artificial Neural Networks on Simulated Resistive Crossbar Arrays. - Malte J. Rasch; Tayfun Gokmen; Wilfried Haensch (IEEE Des. Test, 2020)

A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays. - Malte J. Rasch; Diego Moreda; Tayfun Gokmen; Manuel Le Gallo; Fabio Carta; Cindy Goldberg; Kaoutar El Maghraoui; Abu Sebastian; Vijay Narayanan (AICAS, 2021)

Toward Software-Equivalent Accuracy on Transformer-Based Deep Neural Networks With Analog Memory Devices. - Katie Spoon; Hsinyu Tsai; An Chen 002; Malte J. Rasch; Stefano Ambrogio; Charles Mackin; Andrea Fasoli; Alexander M. Friz; Pritish Narayanan; Milos Stanisavljevic; Geoffrey W. Burr (Frontiers Comput. Neurosci., 2021)

Analog-memory-based 14nm Hardware Accelerator for Dense Deep Neural Networks including Transformers. - Atsuya Okazaki; Pritish Narayanan; Stefano Ambrogio; Kohji Hosokawa; Hsinyu Tsai; Akiyo Nomura; Takeo Yasuda; Charles Mackin; Alexander M. Friz; Masatoshi Ishii; Yasuteru Kohda; Katie Spoon; et al. (ISCAS, 2022)

Impact of Phase-Change Memory Flicker Noise and Weight Drift on Analog Hardware Inference for Large-Scale Deep Learning Networks. - Jin-Ping Han; Malte J. Rasch; Zuoguang Liu; Paul Solomon; Kevin Brew; Kangguo Cheng; Injo Ok; Victor Chan; Michael Longstreet; Wanki Kim; Robert L. Bruce; Cheng-Wei Cheng; et al. (Adv. Intell. Syst., 2022)

Pattern Training, Inference, and Regeneration Demonstration Using On-Chip Trainable Neuromorphic Chips for Spiking Restricted Boltzmann Machine. - Uicheol Shin; Masatoshi Ishii; Atsuya Okazaki; Megumi Ito; Malte J. Rasch; Wanki Kim; Akiyo Nomura; Wonseok Choi 012; Dooyong Koh; Kohji Hosokawa; Matthew BrightSky; Seiji Munetoh; et al. (Adv. Intell. Syst., 2022)

AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing. - Hadjer Benmeziane; Corey Lammie; Irem Boybat; Malte J. Rasch; Manuel Le Gallo; Hsinyu Tsai; Ramachandran Muralidhar; Smaïl Niar; Hamza Ouarnoughi; Vijay Narayanan; Abu Sebastian; Kaoutar El Maghraoui (EDGE, 2023)

Impact of Phase-Change Memory Drift on Energy Efficiency and Accuracy of Analog Compute-in-Memory Deep Learning Inference (Invited). - Martin M. Frank; Ning Li; Malte J. Rasch; Shubham Jain; Ching-Tzu Chen; Ramachandran Muralidhar; Jin-Ping Han; Vijay Narayanan; Timothy Philip; Kevin Brew; Andrew Simon; Iqbal Saraf; et al. (IRPS, 2023)

Architectures and Circuits for Analog-memory-based Hardware Accelerators for Deep Neural Networks (Invited). - Hsinyu Tsai; Pritish Narayanan; Shubham Jain; Stefano Ambrogio; Kohji Hosokawa; Masatoshi Ishii; Charles Mackin; Ching-Tzu Chen; Atsuya Okazaki; Akiyo Nomura; Irem Boybat; Ramachandran Muralidhar; et al. (ISCAS, 2023)

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators. - Malte J. Rasch; Charles Mackin; Manuel Le Gallo; An Chen 002; Andrea Fasoli; Frédéric Odermatt; Ning Li; S. R. Nandakumar; Pritish Narayanan; Hsinyu Tsai; Geoffrey W. Burr; Abu Sebastian; et al. (CoRR, 2023)

Fast offset corrected in-memory training. - Malte J. Rasch; Fabio Carta; Omobayode Fagbohungbe; Tayfun Gokmen (CoRR, 2023)

Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference. - Manuel Le Gallo; Corey Lammie; Julian Büchel; Fabio Carta; Omobayode Fagbohungbe; Charles Mackin; Hsinyu Tsai; Vijay Narayanan; Abu Sebastian; Kaoutar El Maghraoui; Malte J. Rasch (CoRR, 2023)

Analog AI as a Service: A Cloud Platform for In-Memory Computing. - Kaoutar El Maghraoui; Kim Tran; Kurtis Ruby; Borja Godoy; Jordan Murray; Manuel Le Gallo-Bourdeau; Todd Deshane; Pablo Gonzalez; Diego Moreda; Hadjer Benmeziane; Corey Liam Lammie; Julian Büchel; et al. (SSE, 2024)

Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training. - Corey Lammie; Athanasios Vasilopoulos; Julian Büchel; Giacomo Camposampiero; Manuel Le Gallo; Malte J. Rasch; Abu Sebastian (ISCAS, 2024)

Towards Exact Gradient-based Training on Analog In-memory Computing. - Zhaoxian Wu; Tayfun Gokmen; Malte J. Rasch; Tianyi Chen (NeurIPS, 2024)

State-Independent Low Resistance Drift SiSbTe Phase Change Memory for Analog In-Memory Computing Applications. - H. Y. Cheng; Z. L. Liu; A. Majumdar; Alexander Grun; A. Ray; J. Su; Malte J. Rasch; Fabio Carta; Lynne M. Gignac; C. Lavoie; C. W. Cheng; M. Bright Sky; et al. (VLSI Technology and Circuits, 2024)

Multi-Function Multi-Way Analog Technology for Sustainable Machine Intelligence Computation. - Vassilis Kalantzis; Mark S. Squillante; Shashanka Ubaru; Tayfun Gokmen; Chai Wah Wu; Anshul Gupta; Haim Avron; Tomasz Nowicki; Malte J. Rasch; O. Murat Onen; Vanessa López-Marrero; Effendi Leobandung; et al. (CoRR, 2024)

Assessing the Performance of Analog Training for Transfer Learning. - Omobayode Fagbohungbe; Corey Lammie; Malte J. Rasch; Takashi Ando; Tayfun Gokmen; Vijay Narayanan (ISCAS, 2025)

Rapid yet accurate Tile-circuit and device modeling for Analog In-Memory Computing. - Jose Luquin; Charles Mackin; Stefano Ambrogio; An Chen 002; F. Baldi; G. Miralles; Malte J. Rasch; Julian Büchel; M. Lalwani; W. Ponghiran; Paul Solomon; Hsinyu Tsai; et al. (CoRR, 2025)