Advanced Deep Learning

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.

WE PRESENT MAGIC123, A TWO-STAGE COARSE-TO-FINE APPROACH FOR HIGH-QUALITY, TEXTURED 3D MESHES GENERATION FROM A SINGLE UNPOSED IMAGE IN THE WILD USING BOTH2D AND 3D PRIORS.

IN ADDITION, WE FIND VALL-E COULD PRESERVE THE SPEAKER'S EMOTION AND ACOUSTIC ENVIRONMENT OF THE ACOUSTIC PROMPT IN SYNTHESIS.

SINGLE IMAGE 3D RECONSTRUCTION IS AN IMPORTANT BUT CHALLENGING TASK THAT REQUIRES EXTENSIVE KNOWLEDGE OF OUR NATURAL WORLD.

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

SILO IS BUILT BY (1) TRAINING A PARAMETRIC LM ON OPEN LICENSE CORPUS (OLC), A NEW CORPUS WE CURATE WITH 228B TOKENS OF PUBLIC DOMAIN AND PERMISSIVELY LICENSED TEXT AND (2) AUGMENTING IT WITH A MORE GENERAL AND EASILY MODIFIABLE NONPARAMETRIC DATASTORE (E. G., CONTAINING COPYRIGHTED BOOKS OR NEWS) THAT IS ONLY QUERIED DURING INFERENCE.

SEMANTIC PARSING

DURING TRAINING, GENPROMP CONVERTS IMAGE CATEGORY LABELS TO LEARNABLE PROMPT EMBEDDINGS WHICH ARE FED TO A GENERATIVE MODEL TO CONDITIONALLY RECOVER THE INPUT IMAGE WITH NOISE AND LEARN REPRESENTATIVE EMBEDDINGS.