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Found 12 papers, 5 papers with code
JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks
With the rapid advancements in Multimodal Large Language Models (MLLMs), securing these models against malicious inputs while aligning them with human values has emerged as a critical challenge.
Understanding News Creation Intents: Frame, Dataset, and Method
As the disruptive changes in the media economy and the proliferation of alternative news media outlets, news intent has progressively deviated from ethical standards that serve the public interest.
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.
Learning the joint distribution of two sequences using little or no paired data
We present a noisy channel generative model of two sequences, for example text and speech, which enables uncovering the association between the two modalities when limited paired data is available.
Gated Transformer Networks for Multivariate Time Series Classification
In this work, we explored a simple extension of the current Transformer Networks with gating, named Gated Transformer Networks (GTN) for the multivariate time series classification problem.
Reconciling modern machine learning practice and the bias-variance trade-off
This connection between the performance and the structure of machine learning models delineates the limits of classical analyses, and has implications for both the theory and practice of machine learning.
Kernel Machines Beat Deep Neural Networks on Mask-based Single-channel Speech Enhancement
We apply a fast kernel method for mask-based single-channel speech enhancement.
On exponential convergence of SGD in non-convex over-parametrized learning
Large over-parametrized models learned via stochastic gradient descent (SGD) methods have become a key element in modern machine learning.
Kernel machines that adapt to GPUs for effective large batch training
In this paper we develop the first analytical framework that extends linear scaling to match the parallel computing capacity of a resource.
To understand deep learning we need to understand kernel learning
Certain key phenomena of deep learning are manifested similarly in kernel methods in the modern "overfitted" regime.
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
We show that there is a critical batch size $m^*$ such that: (a) SGD iteration with mini-batch size $m\leq m^*$ is nearly equivalent to $m$ iterations of mini-batch size $1$ (\emph{linear scaling regime}).
Diving into the shallows: a computational perspective on large-scale shallow learning
An analysis based on the spectral properties of the kernel demonstrates that only a vanishingly small portion of the function space is reachable after a polynomial number of gradient descent iterations.
