Abstract: Systems and methods for altering the geometry of a fluid channel to prevent upstream mobility of bacteria, using angled obstacles on the interior of the channel that among other things creates vortices that restrict the mobility. An optimized geometry can be realized by an artificial intelligence algorithm or similar methods based on performance of various configurations of obstacle parameters.

Abstract: Systems and methods for altering the geometry of a fluid channel to prevent upstream mobility of bacteria, using angled obstacles on the interior of the channel that among other things creates vortices that restrict the mobility. An optimized geometry can be realized by an artificial intelligence algorithm or similar methods based on performance of various configurations of obstacle parameters.

Abstract: Apparatuses, system, and techniques use one or more first neural networks to generate one or more synthetic data to train one or more second neural networks based, at least in part, on one or more performance metrics of one or more second neural networks.

Abstract: One or more machine learning models (MLMs) may learn implicit 3D representations of geometry of an object and of dynamics of the object from performing an action on the object. Implicit neural representations may be used to reconstruct high-fidelity full geometry of the object and predict a flow-based dynamics field from one or more images, which may provide a partial view of the object. Correspondences between locations of an object may be learned based at least on distances between the locations on a surface corresponding to the object, such as geodesic distances. The distances may be incorporated into a contrastive learning loss function to train one or more MLMs to learn correspondences between locations of the object, such as a correspondence embedding field. The correspondences may be used to evaluate state changes when evaluating one or more actions that may be performed on the object.

Abstract: A machine learning framework is described for performing generation of candidate molecules for, e.g., drug discovery or other applications. The framework utilizes a pre-trained encoder-decoder model to interface between representations of molecules and embeddings for those molecules in a latent space. A fusion module is located between the encoder and decoder and is used to fuse an embedding for an input molecule with embeddings for one or more exemplary molecules selected from a database that is constructed according to a design criteria. The fused embedding is decoded using the decoder to generate a candidate molecule. The fusion module is trained to reconstruct a nearest neighbor to the input molecule from the database based on the sample of exemplary molecules. An iterative approach may be used during inference to dynamically update the database to include newly generated candidate molecules.

Abstract: In various examples, bi-directional projection techniques may be used to generate enhanced Bird's-Eye View (BEV) representations. For example, a system(s) may generate one or more BEV features associated with a BEV of an environment using a projection process that associates 2D image features to one or more first locations of a 3D space. At least partially using the BEV feature(s), the system(s) may determine one or more second locations of the 3D space that correspond to one or more regions of interest in the environment. The system(s) may then generate one or more additional BEV features corresponding to the second location(s) using a different projection process that associates the second location(s) from the 3D space to at least a portion of the 2D image features. The system(s) may then generate an updated BEV of the environment based at least on the BEV feature(s) and/or the additional BEV feature(s).

Abstract: Systems and methods for altering the geometry of a fluid channel to prevent upstream mobility of bacteria, using angled obstacles on the interior of the channel that among other things creates vortices that restrict the mobility. An optimized geometry can be realized by an artificial intelligence algorithm or similar methods based on performance of various configurations of obstacle parameters.

Abstract: Systems and methods for altering the geometry of a fluid channel to prevent upstream mobility of bacteria, using angled obstacles on the interior of the channel that among other things creates vortices that restrict the mobility. An optimized geometry can be realized by an artificial intelligence algorithm or similar methods based on performance of various configurations of obstacle parameters.

Abstract: A vision-language model learns skills and domain knowledge via distinct and separate task-specific neural networks, referred to as experts. Each expert is independently optimized for a specific task, facilitating the use of domain-specific data and architectures that are not feasible with a single large neural network trained for multiple tasks. The vision-language model implemented as an ensemble of pre-trained experts and is more efficiently trained compared with the single large neural network. During training, the vision-language model integrates specialized skills and domain knowledge, rather than trying to simultaneously learn multiple tasks, resulting in effective multi-modal learning.

Abstract: A technique for reconstructing a three-dimensional scene from monocular video adaptively allocates an explicit sparse-dense voxel grid with dense voxel blocks around surfaces in the scene and sparse voxel blocks further from the surfaces. In contrast to conventional systems, the two-level voxel grid can be efficiently queried and sampled. In an embodiment, the scene surface geometry is represented as a signed distance field (SDF). Representation of the scene surface geometry can be extended to multi-modal data such as semantic labels and color. Because properties stored in the sparse-dense voxel grid structure are differentiable, the scene surface geometry can be optimized via differentiable volume rendering.

Abstract: Class agnostic object mask generation uses a vision transformer-based auto-labeling framework requiring only images and object bounding boxes to generate object (segmentation) masks. The generated object masks, images, and object labels may then be used to train instance segmentation models or other neural networks to localize and segment objects with pixel-level accuracy. The generated object masks may supplement or replace conventional human generated annotations. The human generated annotations may be misaligned compared with the object boundaries, resulting in poor quality labeled segmentation masks. In contrast with conventional techniques, the generated object masks are class agnostic and are automatically generated based only on a bounding box image region without relying on either labels or semantic information.

Abstract: A neural network model is trained for fairness and accuracy using both real and synthesized training data, such as images. During training a first sampling ratio between the real and synthesized training data is optimized. The first sampling ratio may comprise a value for each group (or attribute), where each value is optimized. A second sampling ratio defines relative amounts of training data that are used for each one of the groups. Furthermore, a neural network model accuracy and a fairness metric are both used for updating the first and second sampling ratios during training iterations. The neural network model may be trained using different classes of training data. The second sampling ratio may vary for each class.

Abstract: An artificial intelligence framework is described that incorporates a number of neural networks and a number of transformers for converting a two-dimensional image into three-dimensional semantic information. Neural networks convert one or more images into a set of image feature maps, depth information associated with the one or more images, and query proposals based on the depth information. A first transformer implements a cross-attention mechanism to process the set of image feature maps in accordance with the query proposals. The output of the first transformer is combined with a mask token to generate initial voxel features of the scene. A second transformer implements a self-attention mechanism to convert the initial voxel features into refined voxel features, which are up-sampled and processed by a lightweight neural network to generate the three-dimensional semantic information, which may be used by, e.g., an autonomous vehicle for various advanced driver assistance system (ADAS) functions.

Abstract: In various examples, robust trajectory predictions against adversarial attacks in autonomous machines and applications are described herein. Systems and methods are disclosed that perform adversarial training for trajectory predictions determined using a neural network(s). In order to improve the training, the systems and methods may devise a deterministic attach that creates a deterministic gradient path within a probabilistic model to generate adversarial samples for training. Additionally, the systems and methods may introduce a hybrid objective that interleaves the adversarial training and learning from clean data to anchor the output from the neural network(s) on stable, clean data distribution. Furthermore, the systems and methods may use a domain-specific data augmentation technique that generates diverse, realistic, and dynamically-feasible samples for additional training of the neural network(s).

Abstract: A machine learning framework is described for performing generation of candidate molecules for, e.g., drug discovery or other applications. The framework utilizes a pre-trained encoder-decoder model to interface between representations of molecules and embeddings for those molecules in a latent space. A fusion module is located between the encoder and decoder and is used to fuse an embedding for an input molecule with embeddings for one or more exemplary molecules selected from a database that is constructed according to a design criteria. The fused embedding is decoded using the decoder to generate a candidate molecule. The fusion module is trained to reconstruct a nearest neighbor to the input molecule from the database based on the sample of exemplary molecules. An iterative approach may be used during inference to dynamically update the database to include newly generated candidate molecules.

Abstract: A machine learning model (MLM) may be trained and evaluated. Attribute-based performance metrics may be analyzed to identify attributes for which the MLM is performing below a threshold when each are present in a sample. A generative neural network (GNN) may be used to generate samples including compositions of the attributes, and the samples may be used to augment the data used to train the MLM. This may be repeated until one or more criteria are satisfied. In various examples, a temporal sequence of data items, such as frames of a video, may be generated which may form samples of the data set. Sets of attribute values may be determined based on one or more temporal scenarios to be represented in the data set, and one or more GNNs may be used to generate the sequence to depict information corresponding to the attribute values.

Abstract: One or more machine learning models (MLMs) may learn implicit 3D representations of geometry of an object and of dynamics of the object from performing an action on the object. Implicit neural representations may be used to reconstruct high-fidelity full geometry of the object and predict a flow-based dynamics field from one or more images, which may provide a partial view of the object. Correspondences between locations of an object may be learned based at least on distances between the locations on a surface corresponding to the object, such as geodesic distances. The distances may be incorporated into a contrastive learning loss function to train one or more MLMs to learn correspondences between locations of the object, such as a correspondence embedding field. The correspondences may be used to evaluate state changes when evaluating one or more actions that may be performed on the object.

Abstract: Systems and methods for learning based control in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training an adaptive controller. The method includes steps for receiving a set of training data that includes several training samples, wherein each training sample includes a state and a true uncertain effect value. The method includes steps for computing an uncertain effect value based on the state, computing a set of one or more losses based on the true uncertain effect value and the computed uncertain effect value, and updating the adaptive controller based on the computed set of losses.

Abstract: A system such as a service of a computing resource service provider includes executable code that, if executed by one or more processors, causes the one or more processors to initiate a training of a machine-learning model with a parameter for the training having a first value, the training to determine a set of parameters for the model, calculate output of the training, and change the parameter of the training to have a second value during the training based at least in part on the output. Training parameters may, in some cases, also be referred to as hyperparameters.

Abstract: Systems and methods for determining molecular structures based on atomic-orbital-based features are described. Atomic-orbital-based features can be utilized in combination with machine-learning methods to predict accurate properties, such as quantum mechanical energy, of molecular systems.