ADVANCED DEEP LEARNING

RECENTLY, THE DETR FRAMEWORK HAS EMERGED AS THE DOMINANT APPROACH FOR HUMAN--OBJECT INTERACTION (HOI) RESEARCH.

RECENT ADVANCEMENTS IN NEURAL VOCODING ARE PREDOMINANTLY DRIVEN BY GENERATIVE ADVERSARIAL NETWORKS (GANS) OPERATING IN THE TIME-DOMAIN.

MOST METHODS IN THIS DIRECTION DEVELOP TASKSPECIFIC MODELS THAT ARE TRAINED WITH TYPE-SPECIFIC LABELS, SUCH AS MOMENT RETRIEVAL (TIME INTERVAL) AND HIGHLIGHT DETECTION (WORTHINESS CURVE), WHICH LIMITS THEIR ABILITIES TO GENERALIZE TO VARIOUS VTG TASKS AND LABELS.

WE INTRODUCE OPENFLAMINGO, A FAMILY OF AUTOREGRESSIVE VISION-LANGUAGE MODELS RANGING FROM 3B TO 9B PARAMETERS.

OUR ANALYSIS SUGGESTS THAT INSTRUCTOR IS ROBUST TO CHANGES IN INSTRUCTIONS, AND THAT INSTRUCTION FINETUNING MITIGATES THE CHALLENGE OF TRAINING A SINGLE MODEL ON DIVERSE DATASETS.

IN THIS WORK, WE PRESENT A CONCEPTUALLY SIMPLE AND EFFECTIVE METHOD TO TRAIN A STRONG BILINGUAL/MULTILINGUAL MULTIMODAL REPRESENTATION MODEL.

IN THIS WORK, WE MEASURE AND IMPROVE THE FACTUAL ACCURACY OF LARGE-SCALE LMS FOR OPEN-ENDED TEXT GENERATION.

WHISPER IS ONE OF THE RECENT STATE-OF-THE-ART MULTILINGUAL SPEECH RECOGNITION AND TRANSLATION MODELS, HOWEVER, IT IS NOT DESIGNED FOR REAL TIME TRANSCRIPTION.

FURTHERMORE, WE DEMONSTRATE THAT THE POISSON SURFACE RECONSTRUCTION PROBLEM IS WELL-POSED IN THE LIMIT CASE BY SHOWING A UNIVERSAL APPROXIMATION THEOREM FOR THE SOLUTION OPERATOR OF THE POISSON EQUATION WITH DISTRIBUTIONAL DATA UTILIZING THE FOURIER NEURAL OPERATOR, WHICH PROVIDES A THEORETICAL FOUNDATION FOR OUR NUMERICAL RESULTS.

IN THIS WORK, WE EXTEND SUCH AN EDITING FRAMEWORK TO DIFFUSION MODELS AND PROPOSE DRAGDIFFUSION.

THIS PRESENTS A NOVEL APPROACH FOR GENERATING 3D TALKING HEADS FROM RAW AUDIO INPUTS.

THE NEED FOR AUTONOMOUS ROBOT SYSTEMS IN BOTH THE SERVICE AND THE INDUSTRIAL DOMAIN IS LARGER THAN EVER.

LARGE LANGUAGE MODELS (LLMS) HAVE SHOWN EXCELLENT PERFORMANCE ON VARIOUS TASKS, BUT THE ASTRONOMICAL MODEL SIZE RAISES THE HARDWARE BARRIER FOR SERVING (MEMORY SIZE) AND SLOWS DOWN TOKEN GENERATION (MEMORY BANDWIDTH).

FURTHERMORE, WE PROPOSE A SELF-ATTENTION METHOD TO ENHANCE THE ABILITY OF LARGE MODELS TO OVERCOME ERRORS PRESENT IN REFERENCE DATA, FURTHER OPTIMIZING THE ISSUE OF MODEL HALLUCINATIONS AT THE MODEL LEVEL AND IMPROVING THE PROBLEM-SOLVING CAPABILITIES OF LARGE MODELS.

IMPLICIT NEURAL REPRESENTATION HAS EMERGED AS A POWERFUL METHOD FOR RECONSTRUCTING 3D SCENES FROM 2D IMAGES.

WE PRESENT GENTOPIA, AN ALM FRAMEWORK ENABLING FLEXIBLE CUSTOMIZATION OF AGENTS THROUGH SIMPLE CONFIGURATIONS, SEAMLESSLY INTEGRATING VARIOUS LANGUAGE MODELS, TASK FORMATS, PROMPTING MODULES, AND PLUGINS INTO A UNIFIED PARADIGM.

WE INTRODUCE VOYAGER, THE FIRST LLM-POWERED EMBODIED LIFELONG LEARNING AGENT IN MINECRAFT THAT CONTINUOUSLY EXPLORES THE WORLD, ACQUIRES DIVERSE SKILLS, AND MAKES NOVEL DISCOVERIES WITHOUT HUMAN INTERVENTION.

THE FIVE-DOLLAR MODEL IS A LIGHTWEIGHT TEXT-TO-IMAGE GENERATIVE ARCHITECTURE THAT GENERATES LOW DIMENSIONAL IMAGES FROM AN END TEXT PROMPT.

TO ADDRESS THE CHALLENGES OF ACHIEVING AUTONOMOUS COOPERATION, WE PROPOSE A NOVEL COMMUNICATIVE AGENT FRAMEWORK NAMED ROLE-PLAYING.

WE PRESENT SADTALKER, WHICH GENERATES 3D MOTION COEFFICIENTS (HEAD POSE, EXPRESSION) OF THE 3DMM FROM AUDIO AND IMPLICITLY MODULATES A NOVEL 3D-AWARE FACE RENDER FOR TALKING HEAD GENERATION.