Nec Corporation

Snap Inc.

Google Inc.

Ligong Han

Hans Peter Graf

Renqiang Min

Sergey Tulyakov

Peyman Milanfar

Jian Ren

Han Zhang

Hsin-Ying Lee

Kyle Olszewski

Yinxiao Li

Shervin Minaee

Francesco Barbieri

A method generates new binding peptides to Major Histocompatibility Complex (MHC) proteins. The method includes training a Generative Adversarial Network (GAN) having a generator and a discriminator only on a set of binding peptide sequences given training data comprising the set of binding peptide sequences and a set of non-binding peptide sequences. A GAN training objective includes the discriminator being iteratively updated to distinguish generated peptide sequences from sampled binding peptide sequences as fake or real and the generator being updated to fool the discriminator. The GAN training objective is optimized while learning two projection vectors for a binding class with two cross-entropy losses. A first loss discriminates binding peptide sequences in the training data from non-binding peptide sequences in the training data. A second loss discriminates generated binding peptide sequences from non-binding peptide sequences in the training data. The method enables supporting decision-making related to new binding peptide generation.

Peptide based vaccine generation system with dual projection generative adversarial networks

A method generates new binding peptides to Major Histocompatibility Complex (MHC) proteins. The method includes training, a Generative Adversarial Network (GAN) having a generator and a discriminator only on a set of binding peptide sequences given training data comprising the set of binding peptide sequences and a set of non-binding peptide sequences. A GAN training objective includes the discriminator being iteratively updated to distinguish generated peptide sequences from sampled binding peptide sequences as fake or real and the generator being updated to fool the discriminator. The GAN training objective is optimized while learning two projection vectors for a binding class with two cross-entropy losses. A first loss discriminates binding peptide sequences in the training data from non-binding peptide sequences in the training data. A second loss discriminates generated binding peptide sequences from non-binding peptide sequences in the training data. The method enables supporting decision-making related to new binding peptide generation.

A multimodal video generation framework (MMVID) that benefits from text and images provided jointly or separately as input. Quantized representations of videos are utilized with a bidirectional transformer with multiple modalities as inputs to predict a discrete video representation. A new video token trained with self-learning and an improved mask-prediction algorithm for sampling video tokens is used to improve video quality and consistency. Text augmentation is utilized to improve the robustness of the textual representation and diversity of generated videos. The framework incorporates various visual modalities, such as segmentation masks, drawings, and partially occluded images. In addition, the MMVID extracts visual information as suggested by a textual prompt.

Video synthesis via multimodal conditioning

A method for learning disentangled representations of videos is presented. The method includes feeding each frame of video data into an encoder to produce a sequence of visual features, passing the sequence of visual features through a deep convolutional network to obtain a posterior of a dynamic latent variable and a posterior of a static latent variable, sampling static and dynamic representations from the posterior of the static latent variable and the posterior of the dynamic latent variable, respectively, concatenating the static and dynamic representations to be fed into a decoder to generate reconstructed sequences, and applying three regularizers to the dynamic and static latent variables to trigger representation disentanglement. To facilitate the disentangled sequential representation learning, orthogonal factorization in generative adversarial network (GAN) latent space is leveraged to pre-train a generator as a decoder in the method.

Learning orthogonal factorization in GAN latent space

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Edison, NJ, United States of America

Ligong Han