Large deviations for the asymptotics of Ziv-Lempel codes for 2-D Gibbs fields. - Yali Amit; Michael I. Miller (IEEE Trans. Inf. Theory, 1992)

Large deviations for coding Markov chains and Gibbs random fields. - Yali Amit; Michael I. Miller (IEEE Trans. Inf. Theory, 1993)

Deformable template models for emission tomography. - Yali Amit; Kevin M. Manbeck (IEEE Trans. Medical Imaging, 1993)

A Nonlinear Variational Problem for Image Matching. - Yali Amit (SIAM J. Sci. Comput., 1994)

Graphical Templates for Model Registration. - Yali Amit; Augustine Kong (IEEE Trans. Pattern Anal. Mach. Intell., 1996)

Shape Quantization And Recognition With Randomized Trees. - Yali Amit; Donald Geman (Neural Comput., 1997)

Joint Induction of Shape Features and Tree Classifiers. - Yali Amit; Kenneth Wilder (IEEE Trans. Pattern Anal. Mach. Intell., 1997)

Graphical Shape Templates for Automatic Anatomy Detection with Applications to MRI Brain Scans. - Yali Amit (IEEE Trans. Medical Imaging, 1997)

A Computational Model for Visual Selection. - Yali Amit; Donald Geman (Neural Comput., 1999)

A Neural Network Architecture for Visual Selection. - Yali Amit (Neural Comput., 2000)

Attractor Networks for Shape Recognition. - Yali Amit; Massimo Mascaro (Neural Comput., 2001)

Speech recognition using randomized relational decision trees. - Yali Amit; Alejandro Murua (IEEE Trans. Speech Audio Process., 2001)

A Coarse-to-Fine Strategy for Multiclass Shape Detection. - Yali Amit; Donald Geman; Xiaodong Fan (IEEE Trans. Pattern Anal. Mach. Intell., 2004)

Part-Based Statistical Models for Object Classification and Detection. - Elliot Joel Bernstein; Yali Amit (CVPR (2), 2005)

Generative Models for Labeling Multi-object Configurations in Images. - Yali Amit; Alain Trouvé (Toward Category-Level Object Recognition, 2006)

Generative Model and Consistent Estimation Algorithms for Non-Rigid Deformable Models. - Stéphanie Allassonnière; Estelle Kuhn; Alain Trouvé; Yali Amit (ICASSP (5), 2006)

POP: Patchwork of Parts Models for Object Recognition. - Yali Amit; Alain Trouvé (Int. J. Comput. Vis., 2007)

Optimizing One-Shot Learning with Binary Synapses. - Sandro Romani; Daniel J. Amit; Yali Amit (Neural Comput., 2008)

Efficient Annotation of Vesicle Dynamics Video Microscopy. - Leandro Cortes; Yali Amit (IEEE Trans. Pattern Anal. Mach. Intell., 2008)

Precise Capacity Analysis in Binary Networks with Multiple Coding Level Inputs. - Yali Amit; Yibi Huang (Neural Comput., 2010)

Capacity analysis in multi-state synaptic models: a retrieval probability perspective. - Yibi Huang; Yali Amit (J. Comput. Neurosci., 2011)

Recurrent network of perceptrons with three state synapses achieves competitive classification on real inputs. - Yali Amit; Jacob Walker (Frontiers Comput. Neurosci., 2012)

Memory Capacity of Networks with Stochastic Binary Synapses. - Alexis M. Dubreuil; Yali Amit; Nicolas Brunel (PLoS Comput. Biol., 2014)

Object Detection. - Yali Amit; Pedro F. Felzenszwalb (Computer Vision, A Reference Guide, 2014)

Compact Part-Based Image Representations: Extremal Competition and Overgeneralization. - Marc Goessling; Yali Amit (ICLR (Workshop), 2015)

Sparse Autoregressive Networks. - Marc Goessling; Yali Amit (CoRR, 2015)

A Generative Statistical Algorithm for Automatic Detection of Complex Postures. - Stanislav Nagy; Marc Goessling; Yali Amit; David Biron (PLoS Comput. Biol., 2015)

Dynamic Partition Models. - Marc Goessling; Yali Amit (CoRR, 2017)

Deformable Classifiers. - Jiajun Shen; Yali Amit (CoRR, 2017)

Detecting Curved Edges in Noisy Images in Sublinear Time. - Yi-Qing Wang; Alain Trouvé; Yali Amit; Boaz Nadler (J. Math. Imaging Vis., 2017)

Biologically plausible deep learning. - Yali Amit (CoRR, 2018)

Generative Latent Flow: A Framework for Non-adversarial Image Generation. - Zhisheng Xiao; Qing Yan; Yi-an Chen; Yali Amit (CoRR, 2019)

A Method to Model Conditional Distributions with Normalizing Flows. - Zhisheng Xiao; Qing Yan; Yali Amit (CoRR, 2019)

Deep Learning With Asymmetric Connections and Hebbian Updates. - Yali Amit (Frontiers Comput. Neurosci., 2019)

Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder. - Zhisheng Xiao; Qing Yan; Yali Amit (NeurIPS, 2020)

Exponential Tilting of Generative Models: Improving Sample Quality by Training and Sampling from Latent Energy. - Zhisheng Xiao; Qing Yan; Yali Amit (CoRR, 2020)

EBMs Trained with Maximum Likelihood are Generator Models Trained with a Self-adverserial Loss. - Zhisheng Xiao; Qing Yan; Yali Amit (CoRR, 2021)

Do We Really Need to Learn Representations from In-domain Data for Outlier Detection? - Zhisheng Xiao; Qing Yan; Yali Amit (CoRR, 2021)

Detection Selection Algorithm: A Likelihood based Optimization Method to Perform Post Processing for Object Detection. - Angzhi Fan; Benjamin Ticknor; Yali Amit (CoRR, 2022)

Biologically Plausible Training Mechanisms for Self-Supervised Learning in Deep Networks. - Mufeng Tang; Yibo Yang; Yali Amit (Frontiers Comput. Neurosci., 2022)