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Large language models (LLMs) have revolutionized various AI-infused applications, from chat models to autonomous driving. This evolution has spurred the need for systems that can efficiently deploy and serve these models, especially under the increasing demand for handling long-prompt workloads. The major hurdle in this domain has been balancing high throughput and low latency in…

Large Language Models are being used in various fields. With the growth of AI, the use of LLMs has further increased. They are used in various applications together with those that require reasoning, such as answering multiple-turn questions, completing tasks, and generating code. However, these models are not completely reliable as they may provide inaccurate…

Task-agnostic model pre-training is now the norm in Natural Language Processing, driven by the recent revolution in large language models (LLMs) like ChatGPT. These models showcase proficiency in tackling intricate reasoning tasks, adhering to instructions, and serving as the backbone for widely used AI assistants. Their success is attributed to a consistent enhancement in performance…

Reinforcement learning (RL) has witnessed significant strides in integrating Transformer architectures, which are known for their proficiency in handling long-term dependencies in data. This advancement is crucial in RL, where algorithms learn to make sequential decisions, often in complex and dynamic environments. The fundamental challenge in RL is twofold: understanding and utilizing past observations (memory)…

Aging involves the gradual accumulation of damage and is an important risk factor for chronic diseases. Epigenetic mechanisms, particularly DNA methylation, play a role in aging, though the specific biological processes remain unclear. Epigenetic clocks accurately estimate biological age based on DNA methylation, but their underlying algorithms and key aging processes must be better understood….

Despite the astonishing developments and achievements in the technology field, classical diffusion models still face challenges in image generation, particularly because of their slow sampling speed and the need for extensive parameter tuning. These models, used in computer vision and graphics, have become significant in tasks like synthetic data creation and aiding multi-modal models. However,…

Manifold learning, rooted in the manifold assumption, reveals low-dimensional structures within input data, positing that the data exists on a low-dimensional manifold within a high-dimensional ambient space. Deep Manifold Learning (DML), facilitated by deep neural networks, extends to graph data applications. For instance, MGAE leverages auto-encoders in the graph domain to embed node features and…

You may have missed a big development in the ML weather forecasting revolution over the holidays: GenCast: Google DeepMind’s new generative model!  The importance of probabilistic weather forecasting cannot be overstated in various critical domains like flood forecasting, energy system planning, and transportation routing. Being able to accurately gauge the uncertainty in forecasts, especially concerning…

Machine learning’s shift towards personalization has been transformative, particularly in recommender systems, healthcare, and financial services. This approach tailors decision-making processes to align with individuals’ unique characteristics, enhancing user experience and effectiveness. For instance, in recommender systems, algorithms can suggest products or services based on individual purchase histories and browsing behaviors. However, applying this strategy…

Language models, designed to understand and generate text, are essential tools in various fields, ranging from simple text generation to complex problem-solving. However, a key challenge lies in training these models to perform well on complex or ‘hard’ data, often characterized by its specialized nature and higher complexity. The accuracy and reliability of a model’s…