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Found 12 papers, 6 papers with code
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
InternLM-Math: Open Math Large Language Models Toward Verifiable Reasoning
We further explore how to use LEAN to solve math problems and study its performance under the setting of multi-task learning which shows the possibility of using LEAN as a unified platform for solving and proving in math.
BotChat: Evaluating LLMs' Capabilities of Having Multi-Turn Dialogues
In contrast, other LLMs struggle to generate multi-turn dialogues of satisfactory quality due to poor instruction-following capability, tendency to generate lengthy utterances, or limited general capability.
Group Equivariant BEV for 3D Object Detection
Experimental results demonstrate that GeqBevNet can extract more rotational equivariant features in the 3D object detection of the actual road scene and improve the performance of object orientation prediction.
Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents
Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation.
Spatial Location Constraint Prototype Loss for Open Set Recognition
How to reduce the open space risk is the key of open set recognition.
Adversarial Motorial Prototype Framework for Open Set Recognition
Second, this paper proposes the adversarial motorial prototype framework (AMPF) based on the MPF.
Variational Temporal Deep Generative Model for Radar HRRP Target Recognition
We develop a recurrent gamma belief network (rGBN) for radar automatic target recognition (RATR) based on high-resolution range profile (HRRP), which characterizes the temporal dependence across the range cells of HRRP.
Stratified and Time-aware Sampling based Adaptive Ensemble Learning for Streaming Recommendations
To address these problems, we propose a Stratified and Time-aware Sampling based Adaptive Ensemble Learning framework, called STS-AEL, to improve the accuracy of streaming recommendations.
Double-Wing Mixture of Experts for Streaming Recommendations
In VRS-DWMoE, we first devise variational and reservoir-enhanced sampling to wisely complement new data with historical data, and thus address the user preference drift issue while capturing long-term user preferences.
Deep Autoencoding Topic Model with Scalable Hybrid Bayesian Inference
Given a posterior sample of the global parameters, in order to efficiently infer the local latent representations of a document under DATM across all stochastic layers, we propose a Weibull upward-downward variational encoder that deterministically propagates information upward via a deep neural network, followed by a Weibull distribution based stochastic downward generative model.
Deep Latent Dirichlet Allocation with Topic-Layer-Adaptive Stochastic Gradient Riemannian MCMC
It is challenging to develop stochastic gradient based scalable inference for deep discrete latent variable models (LVMs), due to the difficulties in not only computing the gradients, but also adapting the step sizes to different latent factors and hidden layers.
