Molecule discovery is important in various scientific research fields, particularly pharmaceuticals and materials science. While the emergence of Graph Neural Networks (GNNs) has revolutionized this field by enabling the representation of molecules as graphs and facilitating property predictions, it faces difficulties in generalizing across different tasks, requiring substantial task-specific data collection. These approaches show limitations…
Aging is linked to a significant rise in neurodegenerative diseases like Alzheimer’s and cognitive decline. While brain aging involves complex molecular and cellular changes, our understanding of these processes within their spatial context remains limited. Past studies have provided valuable insights into age-related brain changes at a single-cell level but lack comprehensive spatiotemporal resolution. High-throughput…
Imitation learning (IL) is one of the methods in robotics where robots are trained to mimic human actions based on expert demonstrations. This method relies on supervised machine learning and requires significant human-generated data to guide the robot’s behavior. Although effective for complex tasks, imitation learning is limited by the lack of large-scale datasets and…
Multimodal Art Projection (M-A-P) researchers have introduced FineFineWeb, a large open-source automatic classification system for fine-grained web data. The project decomposes the deduplicated Fineweb into 67 unique categories with extensive seed data. Moreover, a comprehensive correlation analysis between vertical categories and common benchmarks and detailed URL and content distribution analysis are conducted. The system provides…
Artificial intelligence has progressed from handling atomic tasks to addressing intricate, real-world problems requiring the integration of multiple specialized models. This approach, known as AI pipelines, allows for seamless task transitions by connecting different models to process diverse data inputs and outputs. These pipelines enable complex applications like multilingual video dubbing, multimodal content moderation, and…
The advent of automatic speech recognition (ASR) technologies has changed the way individuals interact with digital devices. Despite their capabilities, these systems often demand significant computational power and resources. This makes them inaccessible to users with constrained devices or limited access to cloud-based solutions. This disparity underscores an urgent need for innovations that deliver high-quality…
Artificial intelligence (AI) is reshaping the way we approach everyday tasks, simplifying processes, and unlocking new levels of efficiency. AI tools enhance productivity and offer innovative solutions to a wide range of challenges, from managing daily routines to improving communication and decision-making. Whether it’s automating repetitive chores, organizing schedules, or personalizing experiences, AI is becoming…
Designing antibodies with high specificity and binding affinity to diverse therapeutic antigens remains a significant challenge in drug development. Current methods struggle to effectively generate complementarity-determining regions (CDRs) responsible for antigen binding, especially the highly variable heavy chain CDR3 (HCDR3). These difficulties are mainly due to poor generalization of the already existing computational models to…
The field of neural network architectures has witnessed rapid advancements as researchers explore innovative ways to enhance computational efficiency while maintaining or improving model performance. Traditional dense networks rely heavily on computationally expensive matrix operations to encode and store information. This reliance poses challenges when scaling these models for real-world applications that demand extensive knowledge…
Since the release of BERT in 2018, encoder-only transformer models have been widely used in natural language processing (NLP) applications due to their efficiency in retrieval and classification tasks. However, these models face notable limitations in contemporary applications. Their sequence length, capped at 512 tokens, hampers their ability to handle long-context tasks effectively. Furthermore, their…