PYTHON

BATGPT IS A LARGE-SCALE LANGUAGE MODEL DESIGNED AND TRAINED JOINTLY BY WUHAN UNIVERSITY AND SHANGHAI JIAO TONG UNIVERSITY.

THE AVAILABILITY OF LARGE, HIGH-QUALITY DATASETS HAS BEEN ONE OF THE MAIN DRIVERS OF RECENT PROGRESS IN QUESTION ANSWERING (QA).

LARGE LANGUAGE MODEL (LLM) BASED GENERATIVE AI SYSTEMS HAVE SEEN SIGNIFICANT PROGRESS IN RECENT YEARS.

WE TACKLE THE TASK OF CONDITIONAL MUSIC GENERATION.

MULTI-QUERY ATTENTION (MQA), WHICH ONLY USES A SINGLE KEY-VALUE HEAD, DRASTICALLY SPEEDS UP DER INFERENCE.

MOST VISUAL RECOGNITION STUDIES RELY HEAVILY ON CROWD-LABELLED DATA IN DEEP NEURAL NETWORKS (DNNS) TRAINING, AND THEY USUALLY TRAIN A DNN FOR EACH SINGLE VISUAL RECOGNITION TASK, LEADING TO A LABORIOUS AND TIME-CONSUMING VISUAL RECOGNITION PARADIGM.

ADDING THESE DATA IN A LIGHTWEIGHT FINETUNING STEP CAN SIGNIFICANTLY REDUCE SYCOPHANTIC BEHAVIOR ON HELD-OUT PROMPTS.

CALIBRATION-BASED METHODS HAVE DOMINATED RAW IMAGE DENOISING UNDER EXTREMELY LOW-LIGHT ENVIRONMENTS.

MOST WORK ON REWARD LEARNING HAS USED SIMULATED ENVIRONMENTS, BUT COMPLEX INFORMATION ABOUT VALUES IS OFTEN EXPRESSED IN NATURAL LANGUAGE, AND WE BELIEVE REWARD LEARNING FOR LANGUAGE IS A KEY TO MAKING RL PRACTICAL AND SAFE FOR REAL-WORLD TASKS.

LARGE LANGUAGE MODELS (LLMS) HAVE DEMONSTRATED REMARKABLE PERFORMANCE ACROSS A WIDE ARRAY OF NLP TASKS.

WE EXPECT THIS MODULAR DESIGN TO BOOST ACADEMIC PROGRESS AND INDUSTRIAL DEPLOYMENT OF NERF-BASED AUTONOMOUS DRIVING SIMULATION.

IN THIS , WE ADDRESS THIS CHALLENGE, AND PROPOSE GPTQ, A NEW ONE-SHOT WEIGHT QUANTIZATION METHOD BASED ON APPROXIMATE SECOND-ORDER INFORMATION, THAT IS BOTH HIGHLY-ACCURATE AND HIGHLY-EFFICIENT.

OBJECTIVE AND SUBJECTIVE EVALUATIONS SHOW THAT TEXTIT{PHONEME HALLUCINATOR} OUTPERFORMS EXISTING VC METHODS FOR BOTH INTELLIGIBILITY AND SPEAKER SIMILARITY.

FOR 3D OBJECT DETECTION, WE INSTANTIATE THIS METHOD AS FOCALFORMER3D, A SIMPLE YET EFFECTIVE DETECTOR THAT EXCELS AT EXCAVATING DIFFICULT OBJECTS AND IMPROVING PREDICTION RECALL.

INSPIRED BY THE PROMPT LEARNING IN NATURAL LANGUAGE PROCESSING (NLP), WHICH HAS PRESENTED SIGNIFICANT EFFECTIVENESS IN LEVERAGING PRIOR KNOWLEDGE FOR VARIOUS NLP TASKS, WE STUDY THE PROMPTING TOPIC FOR GRAPHS WITH THE MOTIVATION OF FILLING THE GAP BETWEEN PRE-TRAINED MODELS AND VARIOUS GRAPH TASKS.

WE PRESENT THE ALL-SEEING (AS) PROJECT: A LARGE-SCALE DATA AND MODEL FOR RECOGNIZING AND UNDERSTANDING EVERYTHING IN THE OPEN WORLD.

WE PRESENT NNVISR - AN OPEN-SOURCE FILTER PLUGIN FOR THE VAPOURSYNTH VIDEO PROCESSING FRAMEWORK, WHICH FACILITATES THE APPLICATION OF NEURAL NETWORKS FOR VARIOUS KINDS OF VIDEO ENHANCING TASKS, INCLUDING DENOISING, SUPER RESOLUTION, INTERPOLATION, AND SPATIO-TEMPORAL SUPER-RESOLUTION.

IN THIS WORK, WE AIM TO BRIDGE THIS GAP BY CONDUCTING A RETROSPECTIVE ANALYSIS OF RECENT WORKS IN OFFLINE RL AND PROPOSE REBRAC, A MINIMALISTIC ALGORITHM THAT INTEGRATES SUCH DESIGN ELEMENTS BUILT ON TOP OF THE TD3+BC METHOD.

ACCURATE SAMPLING IS IMPORTANT TO PROVIDE A PRECISE COUPLING OF GEOMETRY AND RADIANCE; AND (III) IT ALLOWS EFFICIENT UNSUPERVISED DISENTANGLEMENT OF SHAPE AND APPEARANCE IN VOLUME RENDERING.

LARGE LANGUAGE MODELS (LLMS) HAVE SEEN AN IMPRESSIVE WAVE OF ADVANCES RECENTLY, WITH MODELS NOW EXCELLING IN A VARIETY OF TASKS, SUCH AS MATHEMATICAL REASONING AND PROGRAM SYNTHESIS.