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Language modeling, a critical component of natural language processing, involves the development of models to process and generate human language. This field has seen transformative advancements with the advent of large language models (LLMs). The primary challenge lies in efficiently optimizing these models. Distributed training with multiple devices faces communication latency hurdles, especially when varying…

Model Predictive Control (MPC) has become a key technology in a number of fields, including power systems, robotics, transportation, and process control. Sampling-based MPC has shown effectiveness in applications such as path planning and control, and it is useful as a subroutine in Model-Based Reinforcement Learning (MBRL), all because of its versatility and parallelizability,  Despite…

Future models must receive superior feedback for effective training signals to advance the development of superhuman agents. Current methods often derive reward models from human preferences, but human performance limitations constrain this process. Relying on fixed reward models impedes the ability to enhance learning during Large Language Model (LLM) training. Overcoming these challenges is crucial…

After human annotation is complete, a machine-learning model automatically examines the tagged pictures to generate the same annotations. Since the picture annotation defines the standards the model attempts to meet, any label mistakes are likewise replicated. Image annotation is the process of labeling or categorizing an image with descriptive data that helps identify and classify…

In a remarkable breakthrough, researchers from Google, Carnegie Mellon University, and Bosch Center for AI have a pioneering method for enhancing the adversarial robustness of deep learning models, showcasing significant advancements and practical implications. To set a headstart, the key takeaways from this research can be placed around the following points: Effortless Robustness through Pretrained…

Artificial Intelligence (AI) and Machine Learning (ML) have grown significantly over the past decade or so, making remarkable progress in almost every field. Be it natural language, mathematical reasoning, or even pharmaceuticals, in today’s age, ML is the driving factor behind innovative solutions in these domains. Chemistry is also one such field where ML has…

Subcellular protein localization is key to unlocking the intricate details of cellular function, particularly within the complex milieu of tumor microenvironments. While providing a broad view, traditional proteomics methods often miss the nuanced, sub-cellular information crucial for a complete understanding. Researchers have noted that current techniques, such as immunofluorescence, tend to aggregate protein expressions within…

Many people are now interested in the state space model (SSM) because of how recent research has advanced. Modern SSMs, which derive from the classic state space model, benefit from concurrent training and excel at capturing long-range dependencies. Process sequence data across many activities and modalities using SSM-based methods like linear state-space layers (LSSL), structured…

Large Language Models (LLMs) are crucial to maximizing efficiency in natural language processing. These models, central to various applications ranging from language translation to conversational AI, face a critical challenge in the form of inference latency. This latency, primarily resulting from traditional autoregressive decoding where each token is generated sequentially, increases with the complexity and…

In computing, there’s a common challenge when it comes to speeding up the process of running complex language models, like those used in large language understanding tasks. These models, often known as LLMs, require significant computational power, and researchers are always on the lookout for ways to make them faster and more efficient. Some existing…