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Found 25 papers, 13 papers with code
Reaction-conditioned De Novo Enzyme Design with GENzyme
Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex.
Structure Language Models for Protein Conformation Generation
In this paper, we introduce Structure Language Modeling (SLM) as a novel framework for efficient protein conformation generation.
ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities
Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation of tool-use capabilities.
Apple Intelligence Foundation Language Models
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute.
MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains
Ultimately, MMAU not only sheds light on the capabilities and limitations of LLM agents but also enhances the interpretability of their performance.
MMIDR: Teaching Large Language Model to Interpret Multimodal Misinformation via Knowledge Distillation
Automatic detection of multimodal misinformation has gained a widespread attention recently.
Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations
The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations in silico.
ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation
Protein function annotation is an important yet challenging task in biology.
Structure-Informed Protein Language Model
To address this issue, we introduce the integration of remote homology detection to distill structural information into protein language models without requiring explicit protein structures as input.
Can Large Language Models Understand Context?
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent.
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
Deep generative models (DGMs) have been widely developed for graph data.
MARRS: Multimodal Reference Resolution System
Successfully handling context is essential for any dialog understanding task.
STEER: Semantic Turn Extension-Expansion Recognition for Voice Assistants
In the context of a voice assistant system, steering refers to the phenomenon in which a user issues a follow-up command attempting to direct or clarify a previous turn.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games
In this paper, we offer a surrogate problem which assesses an LLMs's capability to deduce an entity unknown to itself, but revealed to a judge, by asking the judge a series of queries.
Intelligent Assistant Language Understanding On Device
It has recently become feasible to run personal digital assistants on phones and other personal devices.
Str2Str: A Score-based Framework for Zero-shot Protein Conformation Sampling
The dynamic nature of proteins is crucial for determining their biological functions and properties, for which Monte Carlo (MC) and molecular dynamics (MD) simulations stand as predominant tools to study such phenomena.
5IDER: Unified Query Rewriting for Steering, Intent Carryover, Disfluencies, Entity Carryover and Repair
Providing voice assistants the ability to navigate multi-turn conversations is a challenging problem.
A Text-guided Protein Design Framework
Current AI-assisted protein design mainly utilizes protein sequential and structural information.
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
Here we present a multi-modal molecule structure-text model, MoleculeSTM, by jointly learning molecules' chemical structures and textual descriptions via a contrastive learning strategy.
Protein Sequence and Structure Co-Design with Equivariant Translation
Proteins are macromolecules that perform essential functions in all living organisms.
PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding
However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field.
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding.
High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework
Under mild tail assumptions and arbitrarily chosen (working) models for the propensity score (PS) and the outcome regression (OR) estimators, satisfying only some high-level conditions, we establish finite sample performance bounds for the DDR estimator showing its (optimal) $L_2$ error rate to be $\sqrt{s (\log d)/ n}$ when both models are correct, and its consistency and DR properties when only one of them is correct.
