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Found 12 papers, 2 papers with code
MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training
Through careful and comprehensive ablations of the image encoder, the vision language connector, and various pre-training data choices, we identified several crucial design lessons.
Ranked #18 on
Visual Question Answering
on MM-Vet
Recurrent Drafter for Fast Speculative Decoding in Large Language Models
In this paper, we introduce an improved approach of speculative decoding aimed at enhancing the efficiency of serving large language models.
Divide-or-Conquer? Which Part Should You Distill Your LLM?
Recent methods have demonstrated that Large Language Models (LLMs) can solve reasoning tasks better when they are encouraged to solve subtasks of the main task first.
Graph-Based Model-Agnostic Data Subsampling for Recommendation Systems
Most subsampling methods are model-based and often require a pre-trained pilot model to measure data importance via e. g. sample hardness.
NVDiff: Graph Generation through the Diffusion of Node Vectors
Learning to generate graphs is challenging as a graph is a set of pairwise connected, unordered nodes encoding complex combinatorial structures.
Collaborative Anomaly Detection
In recommendation systems, items are likely to be exposed to various users and we would like to learn about the familiarity of a new user with an existing item.
Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data
We first prove that, with imbalanced data, the available information about unknown parameters is only tied to the relatively small number of positive instances, which justifies the usage of negative sampling.
Vertical Federated Learning without Revealing Intersection Membership
In this paper, we propose a vFL framework based on Private Set Union (PSU) that allows each party to keep sensitive membership information to itself.
Random Function Priors for Correlation Modeling
The likelihood model of high dimensional data $X_n$ can often be expressed as $p(X_n|Z_n,\theta)$, where $\theta\mathrel{\mathop:}=(\theta_k)_{k\in[K]}$ is a collection of hidden features shared across objects, indexed by $n$, and $Z_n$ is a non-negative factor loading vector with $K$ entries where $Z_{nk}$ indicates the strength of $\theta_k$ used to express $X_n$.
Fully Supervised Speaker Diarization
In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN).
Ranked #1 on
Speaker Diarization
on Hub5'00 CallHome
Deep Bayesian Nonparametric Tracking
Time-series data often exhibit irregular behavior, making them hard to analyze and explain with a simple dynamic model.
Stochastic Annealing for Variational Inference
We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference.
