Large language models (LLMs) have become a prominent force in the rapidly evolving landscape of artificial intelligence. These models, built primarily on Transformer architectures, have expanded AI’s capabilities in understanding and generating human language, leading to diverse applications. Yet, a notable challenge in this realm is enhancing LLMs for creative writing. While proficient in various…
Meta-learning, a burgeoning field in AI research, has made significant strides in training neural networks to adapt swiftly to new tasks with minimal data. This technique centers on exposing neural networks to diverse tasks, thereby cultivating versatile representations crucial for general problem-solving. Such varied exposure aims to develop universal capabilities in AI systems, an essential…
With the growth of AI, large language models also began to be studied and used in all fields. These models are trained on vast amounts of data on the scale of billions and are useful in fields like health, finance, education, entertainment, and many others. They contribute to various tasks ranging from natural language processing…
In natural language processing, the quest for precision in language models has led to innovative approaches that mitigate the inherent inaccuracies these models may present. A significant challenge is the models’ tendency to produce “hallucinations” or factual errors due to their reliance on internal knowledge bases. This issue has been particularly pronounced in large language…
Enhancing the receptive field of models is crucial for effective 3D medical image segmentation. Traditional convolutional neural networks (CNNs) often struggle to capture global information from high-resolution 3D medical images. One proposed solution is the utilization of depth-wise convolution with larger kernel sizes to capture a wider range of features. However, CNN-based approaches need help…
A team of researchers from the University of Washington has collaborated to address the challenges in the protein sequence design method by using a deep learning-based protein sequence design method, LigandMPNN. The model targets enzymes and small molecule binder and sensor designs. Existing physically based approaches like Rosetta and deep learning-based models like ProteinMPNN are…
In the dynamic field of Artificial Intelligence (AI), the trajectory from one foundational model to another has represented an amazing paradigm shift. The escalating series of models, including Mamba, Mamba MOE, MambaByte, and the latest approaches like Cascade, Layer-Selective Rank Reduction (LASER), and Additive Quantization for Language Models (AQLM) have revealed new levels of cognitive…
Synthesizers, electronic instruments producing diverse sounds, are integral to music genres. Traditional sound design involves intricate parameter adjustments, demanding expertise. Neural networks aid by replicating input sounds, initially optimizing synthesizer parameters. Recent advances focus on optimizing sound directly for high-fidelity reproduction, requiring unsupervised learning for out-of-domain sounds. Differentiable synthesizers enable automatic differentiation crucial for backpropagation,…
The demand for intelligent and efficient digital assistants proliferates in the modern digital age. These assistants are vital for numerous tasks, including communication, learning, research, and entertainment. However, one of the primary challenges users face worldwide is finding digital assistants that can understand and interact effectively in multiple languages. Bilingual or multilingual capabilities are more…
Large-scale pre-trained vision-language models, exemplified by CLIP (Radford et al., 2021), exhibit remarkable generalizability across diverse visual domains and real-world tasks. However, their zero-shot in-distribution (ID) performance faces limitations on certain downstream datasets. Additionally, when evaluated in a closed-set manner, these models often struggle with out-of-distribution (OOD) samples from novel classes, posing safety risks in…
