ADVANCED DEEP LEARNING

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.

SEMANTIC PARSING

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.

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.

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.

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

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.

ON THE ONE HAND, EACH QUERY IS GENERATED BASED ON 2D LANE-AWARE FEATURES AND ADOPTS A HYBRID EMBEDDING TO ENHANCE THE LANE INFORMATION.

WE PRESENT C-EVAL, THE FIRST COMPREHENSIVE CHINESE EVALUATION SUITE DESIGNED TO ASSESS ADVANCED KNOWLEDGE AND REASONING ABILITIES OF FOUNDATION MODELS IN A CHINESE CONTEXT.

THE PROPOSED FC-CLIP, BENEFITS FROM THE FOLLOWING OBSERVATIONS: THE FROZEN CLIP BACKBONE MAINTAINS THE ABILITY OF OPEN-VOCABULARY CLASSIFICATION AND CAN ALSO SERVE AS A STRONG MASK GENERATOR, AND THE CONVOLUTIONAL CLIP GENERALIZES WELL TO A LARGER INPUT RESOLUTION THAN THE ONE USED DURING CONTRASTIVE IMAGE-TEXT PRETRAINING.

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.

APPLICATIONS BUILT ON TOP OF LARGE LANGUAGE MODELS (LLMS) SUCH AS GPT-4 REPRESENT A REVOLUTION IN AI DUE TO THEIR HUMAN-LEVEL CAPABILITIES IN NATURAL LANGUAGE PROCESSING.

TEXT EVALUATION HAS HISTORICALLY POSED SIGNIFICANT CHALLENGES, OFTEN DEMANDING SUBSTANTIAL LABOR AND TIME COST.

TRACKING 3D OBJECTS ACCURATELY AND CONSISTENTLY IS CRUCIAL FOR AUTONOMOUS VEHICLES, ENABLING MORE RELIABLE DOWNSTREAM TASKS SUCH AS TRAJECTORY PREDICTION AND MOTION PLANNING.