Search Results for author:
Found 51 papers, 37 papers with code
Sailor: Open Language Models for South-East Asia
We present Sailor, a family of open language models ranging from 0. 5B to 7B parameters, tailored for South-East Asian (SEA) languages.
Beyond Memorization: The Challenge of Random Memory Access in Language Models
Through carefully-designed synthetic tasks, covering the scenarios of full recitation, selective recitation and grounded question answering, we reveal that LMs manage to sequentially access their memory while encountering challenges in randomly accessing memorized content.
Graph Diffusion Policy Optimization
Recent research has made significant progress in optimizing diffusion models for specific downstream objectives, which is an important pursuit in fields such as graph generation for drug design.
Purifying Large Language Models by Ensembling a Small Language Model
The emerging success of large language models (LLMs) heavily relies on collecting abundant training data from external (untrusted) sources.
Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast
A multimodal large language model (MLLM) agent can receive instructions, capture images, retrieve histories from memory, and decide which tools to use.
Test-Time Backdoor Attacks on Multimodal Large Language Models
Backdoor attacks are commonly executed by contaminating training data, such that a trigger can activate predetermined harmful effects during the test phase.
Locality Sensitive Sparse Encoding for Learning World Models Online
Unfortunately, NN-based models need re-training on all accumulated data at every interaction step to achieve FTL, which is computationally expensive for lifelong agents.
Benchmarking Large Multimodal Models against Common Corruptions
This technical report aims to fill a deficiency in the assessment of large multimodal models (LMMs) by specifically examining the self-consistency of their outputs when subjected to common corruptions.
Automatic Functional Differentiation in JAX
We present a set of primitive operators that serve as foundational building blocks for constructing several key types of functionals.
Zero Bubble Pipeline Parallelism
Pipeline parallelism is one of the key components for large-scale distributed training, yet its efficiency suffers from pipeline bubbles which were deemed inevitable.
Instant3D: Instant Text-to-3D Generation
Once trained, Instant3D is able to create a 3D object for an unseen text prompt in less than one second with a single run of a feedforward network.
Finetuning Text-to-Image Diffusion Models for Fairness
The rapid adoption of text-to-image diffusion models in society underscores an urgent need to address their biases.
Intriguing Properties of Data Attribution on Diffusion Models
Data attribution seeks to trace model outputs back to training data.
On Memorization in Diffusion Models
Looking into this, we first observe that memorization behaviors tend to occur on smaller-sized datasets, which motivates our definition of effective model memorization (EMM), a metric measuring the maximum size of training data at which a learned diffusion model approximates its theoretical optimum.
Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
Distributed Deep Reinforcement Learning (DRL) aims to leverage more computational resources to train autonomous agents with less training time.
LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition
This paper investigates LoRA composability for cross-task generalization and introduces LoraHub, a simple framework devised for the purposive assembly of LoRA modules trained on diverse given tasks, with the objective of achieving adaptable performance on unseen tasks.
NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF
Second, our Repulsive UDF is a novel alternative to the occupancy field used in MCC, significantly improving the quality of 3D object reconstruction.
On Evaluating Adversarial Robustness of Large Vision-Language Models
Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented performance in response generation, especially with visual inputs, enabling more creative and adaptable interaction than large language models such as ChatGPT.
Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows
Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric generative modeling but has not been widely adopted due to its suboptimal generative quality and lack of conditional modeling capabilities.
From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning
Empirical results on various benchmarks validate that the integration of SQL execution leads to significant improvements in zero-shot scenarios, particularly in table reasoning.
Exploring Incompatible Knowledge Transfer in Few-shot Image Generation
To this end, we propose knowledge truncation to mitigate this issue in FSIG, which is a complementary operation to knowledge preservation and is implemented by a lightweight pruning-based method.
A Recipe for Watermarking Diffusion Models
Diffusion models (DMs) have demonstrated advantageous potential on generative tasks.
D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory
Kohn-Sham Density Functional Theory (KS-DFT) has been traditionally solved by the Self-Consistent Field (SCF) method.
Better Diffusion Models Further Improve Adversarial Training
Under the $\ell_\infty$-norm threat model with $\epsilon=8/255$, our models achieve $70. 69\%$ and $42. 67\%$ robust accuracy on CIFAR-10 and CIFAR-100, respectively, i. e. improving upon previous state-of-the-art models by $+4. 58\%$ and $+8. 03\%$.
Bag of Tricks for Training Data Extraction from Language Models
With the advance of language models, privacy protection is receiving more attention.
Does Federated Learning Really Need Backpropagation?
BAFFLE is 1) memory-efficient and easily fits uploading bandwidth; 2) compatible with inference-only hardware optimization and model quantization or pruning; and 3) well-suited to trusted execution environments, because the clients in BAFFLE only execute forward propagation and return a set of scalars to the server.
IHNet: Iterative Hierarchical Network Guided by High-Resolution Estimated Information for Scene Flow Estimation
This approach circulates high-resolution estimated information (scene flow and feature) from the preceding iteration back to the low-resolution layer of the current iteration.
Mutual Information Regularized Offline Reinforcement Learning
In this work, we propose a novel MISA framework to approach offline RL from the perspective of Mutual Information between States and Actions in the dataset by directly constraining the policy improvement direction.
Optical Neural Ordinary Differential Equations
Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance.
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine
EnvPool is open-sourced at https://github. com/sail-sg/envpool.
$O(N^2)$ Universal Antisymmetry in Fermionic Neural Networks
In this paper, we substitute the Slater determinant with a pairwise antisymmetry construction, which is easy to implement and can reduce the computational cost to $O(N^2)$.
CINO: A Chinese Minority Pre-trained Language Model
It covers Standard Chinese, Yue Chinese, and six other ethnic minority languages.
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition
The trade-off between robustness and accuracy has been widely studied in the adversarial literature.
Causal Attention for Interpretable and Generalizable Graph Classification
To endow the classifier with better interpretation and generalization, we propose the Causal Attention Learning (CAL) strategy, which discovers the causal patterns and mitigates the confounding effect of shortcuts.
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?
The fine-tuning of pre-trained language models has a great success in many NLP fields.
LSTM-RPA: A Simple but Effective Long Sequence Prediction Algorithm for Music Popularity Prediction
The traditional trend prediction models can better predict the short trend than the long trend.
Outage Constrained Robust Secure Beamforming in Cognitive Satellite-Aerial Networks
This paper proposes a robust beamforming scheme to enhance the physical layer security (PLS) of multicast transmission in a cognitive satellite and aerial network (CSAN) operating in the millimeter wave frequency band.
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning
The main challenge is that the agent must not forget previous tasks and also adapt to novel tasks in the stream.
Continual Learning from the Perspective of Compression
We compare these approaches in terms of both compression and forgetting and empirically study the reasons that limit the performance of continual learning methods based on variational posterior approximation.
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
We propose Continual-MAML, an online extension of the popular MAML algorithm as a strong baseline for this scenario.
Online Continual Learning with Maximal Interfered Retrieval
Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks.
Online Continual Learning with Maximally Interfered Retrieval
Methods based on replay, either generative or from a stored memory, have been shown to be effective approaches for continual learning, matching or exceeding the state of the art in a number of standard benchmarks.
Conditional Computation for Continual Learning
In this study, we analyze parameter sharing under the conditional computation framework where the parameters of a neural network are conditioned on each input example.
Gradient based sample selection for online continual learning
To prevent forgetting, a replay buffer is usually employed to store the previous data for the purpose of rehearsal.
On the Spectral Bias of Neural Networks
Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with $100\%$ accuracy.
A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing
Moreover, optimal or near-optimal solutions of historical scenarios can be searched offline and stored in advance.
Softmax GAN
In the generator training phase, the target is to assign equal probability to all data points in the batch, each with probability $\frac{1}{M+N}$.
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion.
Correntropy Induced L2 Graph for Robust Subspace Clustering
In this paper, we study the robust subspace clustering problem, which aims to cluster the given possibly noisy data points into their underlying subspaces.
Purine: A bi-graph based deep learning framework
In this paper, we introduce a novel deep learning framework, termed Purine.
Network In Network
With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers.
Ranked #34 on
Image Classification
on SVHN
