Abstract: Energy-based diffusion models for transforming noisy inputs, the energy-based diffusion models including a denoiser model configured to transform a noisy input into a multiple candidate output predictions at each of a plurality of denoising iterations, and an energy-based model configured to transform the candidate output predictions at each denoising iteration into a single output prediction.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using simulation-based inference to inferring a set of parameters such as measurements, from observations, e.g. real world observations. The method uses a score generation neural network to determine scores for individual observations or for groups of observations that are combined and used to iteratively adjust values of the parameters.

Abstract: De novo protein design, the rational design of new proteins from scratch with specific functions and properties, is a grand challenge in molecular biology. Recently, deep generative models have emerged as a novel data-driven tool for protein engineering. However, current diffusion- and flow-based models generally synthesize backbones only, without sequence or side chains, while protein language models often model sequences instead. The present disclosure provides flow-based protein structure generation which can be conditioned on a given fold class.

Abstract: Systems and methods are disclosed that perform a truncated consistency model training framework that includes two stages. For example, in the first stage, embodiments of the present disclosure may train a consistency model using first and second time step samples. The first time step samples may be obtained based on sampling from a plurality of time steps. Following, a truncated time range that does not include all of the time steps from the plurality of time steps is obtained. Then, third time step samples are obtained based on sampling from the truncated time range and fourth time step samples are determined based on the third time step samples and a time step difference. Afterwards, in a second stage, the consistency model is further trained using the third time step samples and the fourth time step samples.

Abstract: Apparatuses, systems, and techniques for adaptive flow matching. In at least one embodiment, input is received, which includes one or more first variables at first scale. An encoder is used to encode the input to provide a base distribution at the first scale. The base distribution is associated with one or more second variables, and the one or more second variables include one or more variables absent from the one or more first variables. A perturbed base distribution is obtained based on the base distribution and an adaptive noise. A diffusion model is used to generate a target distribution at a second scale. The target distribution is associated with the one or more second variables. The second scale is finer than the first scale.

Abstract: The disclosed method for generating proteins includes generating, using a trained machine learning model, a first protein based on a three-dimensional (3D) representation of a spatial layout for the first protein, where generating the first protein comprises applying cross-attention between one or more first tokens associated with the 3D representation and one or more second tokens associated with a second protein.

Abstract: A computer-implemented method comprising: accessing a machine learning, ML, model that is operable to sample a causal graph from a graph distribution describing different possible graphs, wherein nodes represent the different variables of said set and edges represent causation, and the graph distribution comprises a matrix of probabilities of existence and causal direction of potential edges between pairs of nodes, and wherein the ML model is trained to be able to generate a respective simulated value of a selected variable from among said set based on the sampled causal graph. The method further comprises using the ML model to estimate a treatment effect from one or more intervened-on variables on another, target variable from among the variables of said set.