Advanced Deep Learning

WE EXPLORE HOW GENERATING A CHAIN OF THOUGHT -- A SERIES OF INTERMEDIATE REASONING STEPS -- SIGNIFICANTLY IMPROVES THE ABILITY OF LARGE LANGUAGE MODELS TO PERFORM COMPLEX REASONING.

MULTIMODAL LEARNING AIMS TO BUILD MODELS THAT CAN PROCESS AND RELATE INFORMATION FROM MULTIPLE MODALITIES.

REMARKABLY, THIS PIONEERING FRAMEWORK STANDS AS THE FIRST-EVER WORK TO ACHIEVE INSTANCE, PANOPTIC, AND PROMPTABLE SEGMENTATION ON FOOD IMAGES.

WE PRESENT ARTRACK, AN AUTOREGRESSIVE FRAMEWORK FOR VISUAL OBJECT TRACKING.

HQ-SAM IS ONLY TRAINED ON THE INTRODUCED DETASET OF 44K MASKS, WHICH TAKES ONLY 4 S ON 8 GPUS.

IN THIS WORK, WE PROPOSE TO MODEL THE 3D PARAMETER AS A RANDOM VARIABLE INSTEAD OF A CONSTANT AS IN SDS AND PRESENT VARIATIONAL SCORE DISTILLATION (VSD), A PRINCIPLED PARTICLE-BASED VARIATIONAL FRAMEWORK TO EXPLAIN AND ADDRESS THE AFOREMENTIONED ISSUES IN TEXT-TO-3D GENERATION.

WE PROPOSE A UNIFIED PERMUTATION-EQUIVALENT MODELING APPROACH, IE, MODELING MAP ELEMENT AS A POINT SET WITH A GROUP OF EQUIVALENT PERMUTATIONS, WHICH ACCURATELY DESCRIBES THE SHAPE OF MAP ELEMENT AND STABILIZES THE LEARNING PROCESS.

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).

SINCE THE INTRODUCTION OF THE TRANSFORMER MODEL BY VASWANI ET AL. (2017), A FUNDAMENTAL QUESTION HAS YET TO BE ANSWERED: HOW DOES A MODEL ACHIEVE EXTRAPOLATION AT INFERENCE TIME FOR SEQUENCES THAT ARE LONGER THAN IT SAW DURING TRAINING?

WITH THE ADVANCE OF TEXT-TO-IMAGE MODELS (E. G., STABLE DIFFUSION) AND CORRESPONDING PERSONALIZATION TECHNIQUES SUCH AS DREAMBOOTH AND LORA, EVERYONE CAN MANIFEST THEIR IMAGINATION INTO HIGH-QUALITY IMAGES AT AN AFFORDABLE COST.

BY CONTRAST, HUMANS CAN GENERALLY PERFORM A NEW LANGUAGE TASK FROM ONLY A FEW EXAMPLES OR FROM SIMPLE INSTRUCTIONS - SOMETHING WHICH CURRENT NLP SYSTEMS STILL LARGELY STRUGGLE TO DO.

OKAPI INTRODUCES INSTRUCTION AND RESPONSE-RANKED DATA IN 26 DIVERSE LANGUAGES TO FACILITATE THE EXPERIMENTS AND DEVELOPMENT OF FUTURE MULTILINGUAL LLM RESEARCH.

MULTIMODAL LARGE LANGUAGE MODEL (MLLM) RECENTLY HAS BEEN A NEW RISING RESEARCH HOTSPOT, WHICH USES POWERFUL LARGE LANGUAGE MODELS (LLMS) AS A BRAIN TO PERFORM MULTIMODAL TASKS.

WE PRESENT SDXL, A LATENT DIFFUSION MODEL FOR TEXT-TO-IMAGE SYNTHESIS.

THIS TUTORIAL NOTE SUMMARIZES THE PRESENTATION ON ``LARGE MULTIMODAL MODELS: TOWARDS BUILDING AND SURPASSING MULTIMODAL GPT-4'', A PART OF CVPR 2023 TUTORIAL ON ``RECENT ADVANCES IN VISION FOUNDATION MODELS''.

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.