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Found 25 papers, 13 papers with code
Application-Driven Innovation in Machine Learning
As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important.
Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data
This unsupervised approach allows the generated data to capture the vast complexity of the real world while maintaining the precision and scale of synthetic data.
MVTrans: Multi-View Perception of Transparent Objects
Transparent object perception is a crucial skill for applications such as robot manipulation in household and laboratory settings.
Calibration and generalizability of probabilistic models on low-data chemical datasets with DIONYSUS
Deep learning models that leverage large datasets are often the state of the art for modelling molecular properties.
One-shot recognition of any material anywhere using contrastive learning with physics-based rendering
The synthetic images were rendered using giant collections of textures, objects, and environments generated by computer graphics artists.
Machine Learning for a Sustainable Energy Future
Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances at the levels of materials, devices, and systems for the efficient harvesting, storage, conversion, and management of renewable energy.
AlphaFold Accelerates Artificial Intelligence Powered Drug Discovery: Efficient Discovery of a Novel Cyclin-dependent Kinase 20 (CDK20) Small Molecule Inhibitor
The AlphaFold computer program predicted protein structures for the whole human genome, which has been considered as a remarkable breakthrough both in artificial intelligence (AI) application and structural biology.
Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI dataset
We use this to predict 3D models of vessels and their content from a single image.
Ranked #1 on
Single-View 3D Reconstruction
on TransProteus
Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand.
JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design
Inverse molecular design, i. e., designing molecules with specific target properties, can be posed as an optimization problem.
Computer vision for liquid samples in hospitals and medical labs using hierarchical image segmentation and relations prediction
This work explores the use of computer vision for image segmentation and classification of medical fluid samples in transparent containers (for example, tubes, syringes, infusion bags).
Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations
We expect that extending PASITHEA to larger datasets, molecules and more complex properties will lead to advances in the design of new functional molecules as well as the interpretation and explanation of machine learning models.
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning
However, the search space is vast and efficient exploration is desirable when using reinforcement learning agents.
Scientific intuition inspired by machine learning generated hypotheses
Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas.
Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set
Visual recognition of vessels and their contents is essential for performing this task.
Neural Message Passing on High Order Paths
Graph neural network have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures in the graph such as functional groups and molecular geometry.
Graph Deconvolutional Generation
Graph generation is an extremely important task, as graphs are found throughout different areas of science and engineering.
Generator evaluator-selector net for panoptic image segmentation and splitting unfamiliar objects into parts
The generator/evaluator approach for this case consists of two independent convolutional neural nets: a generator net that suggests variety segments corresponding to objects, stuff and parts regions in the image, and an evaluator net that chooses the best segments to be merged into the segmentation map.
Ranked #37 on
Panoptic Segmentation
on COCO test-dev
Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions
We show that our quantum generator is able to learn target probability distributions using either a classical neural network or a variational quantum circuit as the discriminator.
Quantum Physics
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Generative models are becoming a tool of choice for exploring the molecular space.
A framework for algorithm deployment on cloud-based quantum computers
In recent years, the field of quantum computing has significantly developed in both the improvement of hardware as well as the assembly of various software tools and platforms, including cloud access to quantum devices.
Quantum Physics
Quantum computational chemistry
One of the most promising applications of quantum computing is solving classically intractable chemistry problems.
Quantum Physics
Quantum autoencoders for efficient compression of quantum data
The quantum autoencoder is trained to compress a particular dataset of quantum states, where a classical compression algorithm cannot be employed.
Quantum Physics
Space-Filling Curves as a Novel Crystal Structure Representation for Machine Learning Models
While the structure representations based on atom connectivities are prevalent for molecules, two-dimensional descriptors are not suitable for describing molecular crystals.
Bayesian Network Structure Learning Using Quantum Annealing
The logical structure resulting from the mapping has the appealing property that it is instance-independent for a given number of Bayesian network variables, as well as being independent of the number of data cases.
