Search Results for author:
Found 21 papers, 5 papers with code
CRISPR-GPT: An LLM Agent for Automated Design of Gene-Editing Experiments
We showcase the potential of CRISPR-GPT for assisting non-expert researchers with gene-editing experiments from scratch and validate the agent's effectiveness in a real-world use case.
Diffusion Model for Data-Driven Black-Box Optimization
In this paper, we focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization over complex structured variables.
Embodied LLM Agents Learn to Cooperate in Organized Teams
Large Language Models (LLMs) have emerged as integral tools for reasoning, planning, and decision-making, drawing upon their extensive world knowledge and proficiency in language-related tasks.
Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications
We develop methods to identify critical regions that are vital for safety guardrails, and that are disentangled from utility-relevant regions at both the neuron and rank levels.
A 5' UTR Language Model for Decoding Untranslated Regions of mRNA and Function Predictions
The model outperformed the best-known benchmark by up to 42% for predicting the Mean Ribosome Loading, and by up to 60% for predicting the Translation Efficiency and the mRNA Expression Level.
A Variational Auto-Encoder Enabled Multi-Band Channel Prediction Scheme for Indoor Localization
Indoor localization is getting increasing demands for various cutting-edged technologies, like Virtual/Augmented reality and smart home.
Deep Reinforcement Learning for Efficient and Fair Allocation of Health Care Resources
Scarcity of health care resources could result in the unavoidable consequence of rationing.
Scaling In-Context Demonstrations with Structured Attention
However, their capabilities of in-context learning are limited by the model architecture: 1) the use of demonstrations is constrained by a maximum sentence length due to positional embeddings; 2) the quadratic complexity of attention hinders users from using more demonstrations efficiently; 3) LLMs are shown to be sensitive to the order of the demonstrations.
Visual Adversarial Examples Jailbreak Aligned Large Language Models
Recently, there has been a surge of interest in integrating vision into Large Language Models (LLMs), exemplified by Visual Language Models (VLMs) such as Flamingo and GPT-4.
Deep Reinforcement Learning for Cost-Effective Medical Diagnosis
In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost.
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data
Furthermore, the generated distribution based on the estimated score function captures the data geometric structures and converges to a close vicinity of the data distribution.
Provably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a few seed nodes.
Model-based Reinforcement Learning
reinforcement-learning
+1
High Precision Indoor Localization with Dummy Antennas -- An Experimental Study
With the rising demand for indoor localization, high precision technique-based fingerprints became increasingly important nowadays.
Going Beyond Linear RL: Sample Efficient Neural Function Approximation
While the theory of RL has traditionally focused on linear function approximation (or eluder dimension) approaches, little is known about nonlinear RL with neural net approximations of the Q functions.
Optimal Gradient-based Algorithms for Non-concave Bandit Optimization
This work considers a large family of bandit problems where the unknown underlying reward function is non-concave, including the low-rank generalized linear bandit problems and two-layer neural network with polynomial activation bandit problem.
A Short Note on the Relationship of Information Gain and Eluder Dimension
Eluder dimension and information gain are two widely used methods of complexity measures in bandit and reinforcement learning.
Fast Federated Learning in the Presence of Arbitrary Device Unavailability
In this case, the convergence of popular FL algorithms such as FedAvg is severely influenced by the straggling devices.
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective
We then compare the kernel of deep ResNets with that of deep FFNets and discover that the class of functions induced by the kernel of FFNets is asymptotically not learnable, as the depth goes to infinity.
Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective
We then compare the kernel of deep ResNets with that of deep FFNets and discover that the class of functions induced by the kernel of FFNets is asymptotically not learnable, as the depth goes to infinity.
On the Convergence of FedAvg on Non-IID Data
In this paper, we analyze the convergence of \texttt{FedAvg} on non-iid data and establish a convergence rate of $\mathcal{O}(\frac{1}{T})$ for strongly convex and smooth problems, where $T$ is the number of SGDs.
Distributionally Robust Optimization Leads to Better Generalization: on SGD and Beyond
In this paper, we adopt distributionally robust optimization (DRO) (Ben-Tal et al., 2013) in hope to achieve a better generalization in deep learning tasks.
