Advanced Deep Learning

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.

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.

LANGUAGE MODELS ARE INCREASINGLY BEING DEPLOYED FOR GENERAL PROBLEM SOLVING ACROSS A WIDE RANGE OF TASKS, BUT ARE STILL CONFINED TO TOKEN-LEVEL, LEFT-TO-RIGHT DECISION-MAKING PROCESSES DURING INFERENCE.

THE MODELS THAT EXPLICITLY ACCOUNT FOR STRUCTURED OBJECTS, SUCH AS TREES AND SEGMENTATIONS, DID NOT BENEFIT EQUALLY BECAUSE THEY REQUIRE CUSTOM ALGORITHMS THAT ARE DIFFICULT TO IMPLEMENT IN A VECTORIZED FORM.

ADDING TRAINABLE MEMORY TO SELECTIVELY STORE LOCAL AS WELL AS GLOBAL REPRESENTATIONS OF A SEQUENCE IS A PROMISING DIRECTION TO IMPROVE THE TRANSFORMER MODEL.

WE DEVELOP A PROCEDURE FOR INT8 MATRIX MULTIPLICATION FOR FEED-FORWARD AND ATTENTION PROJECTION LAYERS IN TRANSFORMERS, WHICH CUT THE MEMORY NEEDED FOR INFERENCE BY HALF WHILE RETAINING FULL PRECISION PERFORMANCE.

OUR SSLREC PLATFORM COVERS A COMPREHENSIVE SET OF STATE-OF-THE-ART SSL-ENHANCED RECOMMENDATION MODELS ACROSS DIFFERENT SCENARIOS, ENABLING RESEARCHERS TO EVALUATE THESE CUTTING-EDGE MODELS AND DRIVE FURTHER INNOVATION IN THE FIELD.

MULTI-AGENT REINFORCEMENT LEARNING (MARL) HAS BEEN SHOWN EFFECTIVE FOR COOPERATIVE GAMES IN RECENT YEARS.

IN THE PROCESS, WE SPEED UP IMAGE GENERATION BY UP TO 2X AND REDUCE MEMORY CONSUMPTION BY UP TO 5. 6X.

WE INTRODUCE GPT-NEOX-20B, A 20 BILLION PARAMETER AUTOREGRESSIVE LANGUAGE MODEL TRAINED ON THE PILE, WHOSE WEIGHTS WILL BE MADE FREELY AND OPENLY AVAILABLE TO THE PUBLIC THROUGH A PERMISSIVE LICENSE.

TO EFFECTIVELY FUSE LANGUAGE AND VISION MODALITIES, WE CONCEPTUALLY DIVIDE A CLOSED-SET DETECTOR INTO THREE PHASES AND PROPOSE A TIGHT FUSION SOLUTION, WHICH INCLUDES A FEATURE ENHANCER, A LANGUAGE-GUIDED QUERY SELECTION, AND A CROSS-MODALITY DER FOR CROSS-MODALITY FUSION.

IN THIS NOTE, WE EXPLORE INFERENCE-TIME ALIGNMENT THROUGH IN-CONTEXT LEARNING.

TOOLS SERVE AS PIVOTAL INTERFACES THAT ENABLE HUMANS TO UNDERSTAND AND RESHAPE THE WORLD.

AS THIS SCENARIO ALLOWS FOR REVISIONS TO OUR INCREMENTAL TRANSLATIONS, WE ADOPT A RE-TRANSLATION APPROACH TO SIMULTANEOUS TRANSLATION, WHERE THE SOURCE IS REPEATEDLY TRANSLATED FROM SCRATCH AS IT GROWS.

SINGLE-STAGE TEXT-TO-SPEECH MODELS HAVE BEEN ACTIVELY STUDIED RECENTLY, AND THEIR RESULTS HAVE OUTPERFORMED TWO-STAGE PIPELINE SYSTEMS.

BASED ON THIS IDEA, WE PRESENT MEMORY-AND-ANTICIPATION TRANSFORMER (MAT), A MEMORY-ANTICIPATION-BASED APPROACH, TO ADDRESS THE ONLINE ACTION DETECTION AND ANTICIPATION TASKS.