The success of ANNs stems from mimicking simplified brain structures. Neuroscience reveals that neurons interact through various connectivity patterns, known as circuit motifs, which are crucial for processing information. However, most ANNs only model one or two such motifs, limiting their performance across different tasks—early ANNs, like multi-layer perceptrons, organized neurons into layers resembling synapses.… →

Large Language Models (LLMs) have gained significant prominence in recent years, driving the need for efficient GPU utilization in machine learning tasks. However, researchers face a critical challenge in accurately assessing GPU performance. The commonly used metric, GPU Utilization, accessed through nvidia-smi or integrated observability tools, has proven to be an unreliable indicator of actual… →

One of the core challenges in semilocal density functional theory (DFT) is the consistent underestimation of band gaps, primarily due to self-interaction and delocalization errors. This issue complicates the prediction of electronic properties and charge transfer mechanisms. Hybrid DFT, incorporating a fraction of exact exchange energy, offers improved band gap predictions but often requires system-specific… →

A significant challenge in AI-driven game simulation is the ability to accurately simulate complex, real-time interactive environments using neural models. Traditional game engines rely on manually crafted loops that gather user inputs, update game states, and render visuals at high frame rates, crucial for maintaining the illusion of an interactive virtual world. Replicating this process… →

Large-scale language models have made significant progress in generative tasks involving multiple-speaker speech synthesis, music generation, and audio generation. The integration of speech modality into multimodal unified large models has also become popular, as seen in models like SpeechGPT and AnyGPT. These advancements are largely due to discrete acoustic codec representations used from neural codec… →

Large Language Models (LLMs) have made remarkable strides in multimodal capabilities, with closed-source models like GPT-4, Claude, and Gemini leading the field. However, the challenge lies in democratizing AI by making these powerful models accessible to a broader audience. The current limitation is the substantial computational resources required to run state-of-the-art models effectively. This creates… →

Large Language Models (LLMs) have emerged as powerful tools for understanding and generating human-like text. This paper explores the potential of LLMs to shape human perspectives and influence decisions on particular tasks. The researchers investigate using LLMs in persuasion across various domains such as investment, credit cards, insurance, retail, and Behavioral Change Support Systems (BCSS).… →

LAION, a prominent non-profit organization dedicated to advancing machine learning research by developing open and transparent datasets, has recently released Re-LAION 5B. This updated version of the LAION-5B dataset marks a milestone in the organization’s ongoing efforts to ensure the safety and legal compliance of web-scale datasets used in foundational model research. The new dataset… →