Enming Yuan
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View article: Kimi-VL Technical Report
Kimi-VL Technical Report Open
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2.8B paramet…
View article: MoBA: Mixture of Block Attention for Long-Context LLMs
MoBA: Mixture of Block Attention for Long-Context LLMs Open
Scaling the effective context length is essential for advancing large language models (LLMs) toward artificial general intelligence (AGI). However, the quadratic increase in computational complexity inherent in traditional attention mechan…
View article: Kimi k1.5: Scaling Reinforcement Learning with LLMs
Kimi k1.5: Scaling Reinforcement Learning with LLMs Open
Language model pretraining with next token prediction has proved effective for scaling compute but is limited to the amount of available training data. Scaling reinforcement learning (RL) unlocks a new axis for the continued improvement of…
View article: Antisense oligonucleotide depletion of <i>CCDC146</i> is a broad-spectrum therapeutic strategy for ALS
Antisense oligonucleotide depletion of <i>CCDC146</i> is a broad-spectrum therapeutic strategy for ALS Open
Amyotrophic lateral sclerosis (ALS) is a heritable and incurable disease defined by the degeneration of motor neurons (MNs), yet the genetics of ALS remain partially understood. Using a genomic deep learning-powered whole-genome analysis o…
View article: Compressed Interaction Graph based Framework for Multi-behavior Recommendation
Compressed Interaction Graph based Framework for Multi-behavior Recommendation Open
Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-faceted preferences. However, it is challenging to explore mu…
View article: Compressed Interaction Graph based Framework for Multi-behavior Recommendation
Compressed Interaction Graph based Framework for Multi-behavior Recommendation Open
Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-faceted preferences. However, it is challenging to explore mu…
View article: Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks Open
Sequential recommendation (SR) plays an important role in personalized\nrecommender systems because it captures dynamic and diverse preferences from\nusers' real-time increasing behaviors. Unlike the standard autoregressive\ntraining strat…
View article: Multi-Behavior Sequential Transformer Recommender
Multi-Behavior Sequential Transformer Recommender Open
In most real-world recommender systems, users interact with items in a sequential and multi-behavioral manner. Exploring the fine-grained relationship of items behind the users' multi-behavior interactions is critical in improving the perf…
View article: Understanding the phase separation characteristics of nucleocapsid protein provides a new therapeutic opportunity against SARS-CoV-2
Understanding the phase separation characteristics of nucleocapsid protein provides a new therapeutic opportunity against SARS-CoV-2 Open
The ongoing coronavirus disease 2019 (COVID-19) pandemic has raised an urgent need to develop effective therapeutics against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As a potential antiviral drug target, the nucleo…
View article: A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19
A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19 Open
The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis app…