text stringlengths 1 1k โ | title stringclasses 230
values |
|---|---|
2 Related Works
Diffusion models (DMs) learn the data distribution by denoising and recovering the original data.
Deep Diffusion Process (DDP) [45] adopts a sequence of reversible diffusion steps to model image
probability distribution. It uses a reversible encoder to map the input image to a latent space and
a decode... | Any-to-Any Generation via Composable Diffusion |
During the initial pretraining stage, the model is designed to acquire vision-language knowledge from
a large collection of aligned image-text pairs. We regard the output from the injected projection layer
as a soft prompt for the LLM, prompting it to generate the corresponding ground-truth texts.
Throughout the entire... | MiniGPT-4- Enhancing Vision-Language Understanding with Advanced Large Language Models |
3
Figure 3: Examples of (cid:104)input, chain of thought, output(cid:105) triples for arithmetic, commonsense, and
symbolic reasoning benchmarks. Chains of thought are highlighted. Full prompts in Appendix G. | Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
Recent years have witnessed the rapid development of large language models (LLMs) which emerge as
the favored approach for various applications and demonstrate multi-dimensional abilities, including
instruction following [6, 49, 59], coding assistance [7, 32, 39, 45], and mathematical problem-solving
[13, 26, 38, 69]. ... | METAMATH |
have indeed bridged a major gap in knowledge.
3. Interpret your hypothesis and problem statement with evidence from your literature review section and
give logical reasoning that what you have claimed is in fact true (Donโt worry; if it is negative or positive
still significant). For example, a study ... | How to Write Your PhD Proposal- A Step-By-Step Guide |
Delta Lake is the foundation of the Databricks
Lakehouse. The Delta Lake format encompasses
structured, unstructured and semi-structured
data. Use has surged over the past 2 years.
When compared to the steady, flat or declining
growth in other storage formats (e.g., text, JSON
and CSV), our data shows that a g... | 2023 state of ai databrick |
2. Related work
Mesh-based statistical models. Mesh-based statistical
body models [29, 38, 48, 53, 64] are a popular explicit repre-
sentation for 3D human reconstruction. This is not only be-
cause such models capture the statistics across a human popu-
lation, but also because meshes are compatible with standard
2 | ICON |
3.2.2 Other architecture | Beyond Efficiency |
14/11/2023, 13:39
The Future of Music: How Generative AI Is Transforming the Music Industry | Andreessen Horowitz
TA B L E O F C O N T E N T S
๎ค
The Future of Music: How Generative
AI Is Transforming the Music Industry
Justine Moore and Anish Acharya
SHARE ๎ค
Posted November 9, 2023
Itโs been an eventful year... | The Future of Music_ How Generative AI Is Transforming the Music Industry _ Andreessen Horowitz |
N
e
a
r
e
s
t
N
e
i
g
h
b
o
r
s
)
:
T
h
e
m
a
i
n
i
n
n
o
v
a
t
i
o
n
i
n
S
c
a
N
N
i
s
a
n
i
s
o
t
r
o
p
i
c
v
e
c
t
o
r
q
u
a
n
t
i
z
a
t
i
o
n
.
I
t
q
u
a
n
t
i
z
e
s
a
d
a
t
a
p
o
i
n
t
t
o
s
u
c
h
t
h
a
t
t
h
e
i
n
n
e
r
p
r
o
d
u
c
t
i
s
a
s
s
i
m
i
l
a
r
t
o
... | LLM Powered Autonomous Agents _ Lil'Log |
while computing p(x1, x2, x3); (ii) for any sum or product unit, if all its children have probability 1,
it also has probability 1 following Eq. (2). Finally, although the activations of the PC units in Group
#3 will change when computing p(x1, x2, x3), we do not need to explicitly evaluate these units โ the
root nodeโ... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
r
a
t
i
o
n
r
a
t
e
e
x
c
e
e
d
s
t
h
e
c
o
m
p
l
e
t
i
o
n
r
a
t
e
.
T
h
i
s
m
a
y
l
e
a
d
t
o
i
n
e
f
f
i
c
i
e
n
c
i
e
s
a
n
d
a
n
i
n
a
b
i
l
i
t
y
t
o
m
a
n
a
g
e
t
a
s
k
s
e
f
f
e
c
t
i
v
e
l
y
.
E
n
s
u
r
i
n
g
p
r
o
p
e
r
t
a
s
k
s
e
q
u
e
n
c
i
n
g
a
n
d
p
a
r
a
l
l
e
... | Task-driven Autonomous Agent Utilizing GPT-4, Pinecone, and LangChain for Diverse Applications โ Yohei Nakajima |
โข The printed edition of โStudy Abroad at UCLโ provides an overview of UCLโs study
abroad offering. More detailed information is hosted in the online edition. This
information is published in September of each year and is targeted towards students
intending to begin affiliate studies in either the September twelve... | UCL Academic Manual |
โข Same Tasks, Different Datasets (STDD): Following [40, 41, 60, 61], we also evaluate DocLLM on held-out
datasets. More precisely, we instruction-tune the pre-trained checkpoint of DocLLM on prompts from 11 of
the 16 datasets considered in SDDS, then evaluate DocLLM on the test split of the remaining three datasets.
Th... | DOCLLM |
The cassini, shapes, and smiley simulations are all available in the mlbench R package; the twomoons problem
is available in the fdm2id R package. Default parameters were used throughout, with ๏ฌxed sample size n = 2000.
B.1 Simulations
B.2 Twenty Datasets
The Twenty Datasets benchmark was originally proposed by Van ... | Adversarial Random Forests for Density Estimation and Generative Modeling |
groundedness, informativeness, and citation accuracy labels of a given response are determined by majority voting. All
of the ๏ฌne-tuning and evaluation datasets are in English. | LaMDA- Language Models for Dialog Applications |
[59] Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi. Self-instruct:
Aligning language model with self generated instructions. arXiv preprint arXiv:2212.10560,
2022.
[60] Y. Wang, S. Mishra, P. Alipoormolabashi, Y. Kordi, A. Mirzaei, A. Arunkumar, A. Ashok, A. S.
Dhanasekaran, A. Naik... | QLORA |
H., Liu, Z., Liu, F., Maggioni, M., Mahendru, A., Maynez, J., Misra, V., Moussalem, M., Nado,
Z., Nham, J., Ni, E., Nystrom, A., Parrish, A., Pellat, M., Polacek, M., Polozov, A., Pope, R.,
Qiao, S., Reif, E., Richter, B., Riley, P., Ros, A. C., Roy, A., Saeta, B., Samuel, R., Shelby, R.,
Slone, A., Smilkov, D., So, D.... | TinyLlama |
25.5 15.0
65.4 63.0
19
A.2 BBSH
BBH refers to a subset of difficult tasks from BIG-Bench, handpicked by [48] in 2022, where the
model proposed by [47] in the same year outperformed the average human rater. [48] mentions 23
tasks, two of which consist of three subtasks each. For ease of interpretation, we treat thes... | Mixture-of-Experts |
We show results from all four scoring strategies in Table 4. The best per-
forming strategy is to take the product of step-level scores and to consider the
neutrals as positives, but the difference in performance between all strategies
is minor. Throughout the rest of this work, we consider neutral steps to be
positive... | Letโs Verify Step by Step |
I was fortunate to be part of a summer research experience as an
undergraduate, which took place in Costa Rica. It allowed me to gain
hands-on experience in research while living abroad in the Cabo
Blanco Absolute Reserve. My first project introduced me to shell
taphonomy; I studied the diversity of shells found ... | research statement |
[25] Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander H. Miller, Kurt Shuster, Jack Urbanek, Douwe
Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, Shrimai Prabhumoye, Alan W. Black, Alexander I. Rudnicky,
Jason Williams, Joelle Pineau, Mikhail S. Burtsev, and Jason Weston. The second conversational intelligenc... | LaMDA- Language Models for Dialog Applications |
2. Model Architecture
Gemini models build on top of Transformer decoders (Vaswani et al., 2017) that are enhanced with
improvements in architecture and model optimization to enable stable training at scale and optimized
inference on Googleโs Tensor Processing Units. They are trained to support 32k context length,
emplo... | gemini_1_report |
in [43]. To train this network, we ๏ฌrst predict 10000 depth
maps using the depth estimation models and add continuous
(D + ฯ1) ยท D1/ฯ2 where D is the depth; ฯ1 and ฯ2 indicate
the shift and scale factors, which are randomly sampled in
the range [0, 1] and [30, 50], respectively. Then, we use the
noisy depth maps as inp... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
23 | Beyond Efficiency |
11
12.5%7.2*1047.2*104 JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models | JARVIS-1 |
[AngIE, 2023] AngIE. Angle-optimized text embeddings.
https://github.com/SeanLee97/AnglE, 2023.
[Arora et al., 2023] Daman Arora, Anush Kini, Sayak Ray
Chowdhury, Nagarajan Natarajan, Gaurav Sinha, and
Amit Sharma. Gar-meets-rag paradigm for zero-shot infor-
mation retrieval. arXiv preprint arXiv:2310.20158, 2023.
[A... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
8. Decisions on the admission of applicants are final and there is no right of appeal against such
decisions except as outlined in Section 3.10 Appeal of Entry Decisions.
9. UCL will consider a complaint relating to an applicant for admission only if it is in relation to
process and procedure. Complaints relati... | UCL Academic Manual |
31/08/2023, 09:32
Data Scientist/Machine Learning Engineer (Singapore-based, relocation provided) - Careers at Agoda
Home
Jobs
Teams
Locations
Life at Agoda ๏
Students
Blog
Data Scientist/Machine Learning Engineer
(Singapore-based, relocation provided)
Apply Now
Singapore
About Agodaย
Ago... | Data Scientist_Machine Learning Engineer (Singapore-based, relocation provided) - Careers at Agoda |
โI want to move!โ
One day, a little girl saw the pumpkin. She was only three years old. She smiled and said, โHello, pumpkin!โ
The pumpkin was so happy. It said, โHello, little girl!โ
The little girl smiled and said, โCan I help you move?โ
The pumpkin said, โYes, please!โ
So, the little girl and the pumpkin moved toget... | TinyStories-HowSmallCanLanguageModelsBeandStillSpeak CoherentEnglish? |
A.5SearchEngineA.5SearchEngineInstruction:Youareahelpfulassistanttomakemultiplechoices.YouhaveaccesstoseveralAPIs:(1)Search(query:str):searchBingforaqueryandreturntheonewebpagewiththemostrelevantresults.(2)LoadPage(idx:int):loadthepagereturnedbyBingtoinvestigatethefullcontent.Giveanindex(1,2,or3)ofthepageastheinputofth... | Tool Learning with Foundation Models |
ing our neural blend skinning model (Sec 3.2) on an eagle
sequence, which is challenging due to its large wing ar-
ticulations.
If we swap neural blend skinning for MLP-
SE(3) [33], the reconstruction is less regular. If we swap for
MLP-translation [22, 38], we observe ghosting wings due
to wrong geometric registration... | BANMo- Building Animatable 3D Neural Models from Many Casual Videos |
Feature definition
Feature ID
A
๐๐๐๐๐๐๐๐๐ ๐ก๐ค๐๐ฃ๐
๐ ๐๐๐๐ ๐ก๐ค๐๐ฃ๐
๐ ๐ โ
NO
B
C
D
E
F
G
H
I
J
K
L
M
๐ ๐
๐ ๐๐๐๐ ๐ก๐ค๐๐ฃ๐
๐ค๐๐๐๐๐12๐ โ
๐ ๐
๐ ๐ โ
๐บ๐ท๐๐๐๐3๐
๐บ๐ท๐๐๐๐15๐
๐๐๐๐๐๐๐๐๐3๐โ๐ ๐๐๐๐๐
๐ค๐๐๐๐๐3๐
๐ค๐๐๐๐๐8๐
๐๐๐๐๐๐๐๐๐3๐
๐ค๐๐๐๐๏ฟฝ... | Knowledge-graph-based-rich-and-confidentiality-preserving-Ex_2022_Informatio |
1
.
I
n
t
h
i
s
s
e
c
t
i
o
n
,
w
e
s
h
o
w
t
h
a
t
V
C
G
c
o
n
t
r
a
c
t
s
e
x
p
a
n
d
t
h
e
s
e
t
o
f
e
f
f
i
c
i
e
n
t
e
q
u
i
l
i
b
r
i
a
r
e
l
a
t
i
v
e
t
o
t
h
e
s
e
t
o
f
โ
w
e
a
k
l
y
t
r
u
t
h
f
u
l
โ
(
W
T
)
e
q
u
i
l
i
b
r
i
a
w
h
i
c
h
i
s
t
h
e
f
o
c
u
s
... | Principal-agent VCG contracts - ScienceDirect |
R
a
n
d
o
m
O
n
l
y
S
c
o
r
i
n
g
T
o
p
A
n
d
R
a
n
d
o
m
S
c
o
r
i
n
g
Q
u
a
l
i
t
a
t
i
v
e
l
y
,
t
h
e
m
a
i
n
p
a
t
t
e
r
n
w
e
o
b
s
e
r
v
e
i
s
t
h
a
t
t
h
e
o
r
i
g
i
n
a
l
e
x
p
l
a
n
a
t
i
o
n
i
s
t
o
o
b
r
o
a
d
a
n
d
t
h
e
r
e
v
i
s
e
d
e
x
p
l
a
n
a
t
i
o
n
i
s
... | Language models can explain neurons in language models |
1. Introduction
Text-to-speech (TTS) systems synthesize raw speech wave-
forms from given text through several components. With
the rapid development of deep neural networks, TTS sys-
tem pipelines have been simpli๏ฌed to two-stage genera-
tive modeling apart from text preprocessing such as text
normalization and phonem... | ConditionalVariationalAutoencoderwithAdversarialLearningfor End-to-EndText-to-Speech |
1 Introduction | DINOv2- Learning Robust Visual Features without Supervision |
mixed pairs, especially major versus minor. To understand this, we should ex-
plore music theory. Even in major keys (e.g. C major), it is uncommon to use
only major chords. Hence, chord progression like C, G, A min, F are extremely
prominent. Even though a minor chord is present in this progression, the overall
se... | Video2Music |
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier
Martinet, Marie-Anne Lachaux, Timothยดee Lacroix,
Baptiste Rozi`ere, Naman Goyal, Eric Hambro,
Faisal Azhar, Aurelien Rodriguez, Armand Joulin,
Edouard Grave, and Guillaume Lample. 2023.
Llama: Open and efficient foundation language
models.
Ben Wang and Aran Komatsu... | 2023_GPT4All-J_Technical_Report_2 |
14
Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances
in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, pages 13878โ13888. AAAI Press, 2021.
[46] Jordy Van Landeghem, Rubรจn Tito, Lukasz Borchmann, Michal Pietruszka, Pawel Jรณziak, Rafal... | DOCLLM |
Key retrieval.
In Figure 4b, we investigate key retrieval performance in synthetic task. The prompt
consists of a large amount of syntactically valid Python code, with a function returning a scalar inserted at a
specified position. The model is asked to complete an assert statement with the return value of the inserted... | CodeLlama2 |
Inspired by Intrinsic SAID, LoRA (Low-Rank Adaptation)
[11] introduces two trainable low-rank matrices for weight
update.
In LoRA, a down-projection matrix and an up-
projection matrix are utilized in parallel with the query (Q),
key (K), and value (V) matrices in the attention layer of the
transformer, shown in Fig. 3... | Parameter-EfficientFine-TuningMethods |
7 HALLUCINATION IN ABSTRACTIVE SUMMARIZATION
Abstractive summarization aims to extract essential information from source documents and to
generate short, concise, and readable summaries [222]. Neural networks have achieved remarkable
results on abstractive summarization. However, Maynez et al. [125] observe that neural... | SurveyofHallucinationinNatural Language Generation |
We note that our dropout mechanism is a simpler vari-
ant of Rippel et al. [30] which focused on a retrieval setting
and used a different sampling distribution with an additional
sweeping mechanism. In Section 5.2, we show that manu-
ally setting the truncation value t during inference offers a
new way to traverse the ... | A Neural Space-Time Representation for Text-to-Image Personalization |
Similar to other neural compression methods, the proposed lossless compression approach operates
in two main phases โ (i) learn good PC models that approximate the data distribution, and (ii)
compress and decompress samples x with computationally ef๏ฌcient algorithms. The proposed
lossless compression algorithm has four... | LOSSLESS COMPRESSION WITH PROBABILISTIC CIRCUITS |
3.1.2 Reasoning. The task of reasoning poses significant challenges for an intelligent AI model. To
effectively tackle reasoning tasks, the models need to not only comprehend the provided information
but also utilize reasoning and inference to deduce answers when explicit responses are absent.
Table 2 reveals that ther... | ASurveyonEvaluationofLargeLanguageModels |
2 Background
This section introduces the notation we use for representing a planning problem to be solved by
LLMs, and recaps the standard representation of classical planners.
2.1 The Classical Planning Problem
Formally, the input of a planning problem P is de๏ฌned by a tuple (cid:104)S ,sinit , S G, A , f(cid:105):
... | LLM+P- Empowering Large Language Models with Optimal Planning Proficiency |
Table 17: (Cont.) The exemplars are selected on GSM8K train set. This set of exemplars is used by
GSM8K, ASDiv, SVAMP, and SingleEq.
DATASET
AQuA
Iter-CoT(S) Exemplars
Q: A train 360 m long is running at a speed of 45 km/hr. In what time will it pass a bridge 140 m long? Options: A:40
sec B:42 sec C:45 sec D:48 sec... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
We found that heightened expectations (supporting H2.1.) carry over to the way participants
make decisions (RQ2). Participants in the sham-AI condition responded slightly faster and more
accurately when informed they were interacting with an adaptive AI system. Using the DDM model
to analyze decision-making, we found t... | AI enhance sour performance |
Hochschild, J. L., & Einstein, K. L. (2015). Do Facts Matter? Information and
Misinformation in American Politics (1st ed.). Norman: University of Oklahoma
Press.
Holman, M. R., & Lay, J. C. (2019). They see dead people (voting): Correcting
misperceptions about voter fraud in the 2016 U.S. presidential election.
Journ... | Social_Media_and_Democracy |
initial
certain. Initial studies [Wang et al., 2023b] have begun to ad-
dress this, yet the parameter count in RAG models still lags
behind that of LLMs. The possibility of an Inverse Scaling
Law9, where smaller models outperform larger ones, is par-
ticularly intriguing and merits further investigation.
Production-R... | RAG forLargeLanguageModels-ASurvey |
size roughly constant. A number of obvious optimizations fall into this category, and we describe
them below, in addition to several other tweaks that provide marginal but worthwhile/free gains. | CRAMMING-TRAININGALANGUAGEMODELONA SINGLEGPUINONEDAY |
Klyueva, A. (2019). Trolls, bots, and whatnots: Deceptive content, deception detection,
and deception suppression. In I. Chiluwa & S. Samoilenko (Eds.), Handbook of
Research on Deception, Fake News, and Misinformation Online (pp. 18โ32).
Hershey, PA: IGI Global. https://doi.org/10.4018/978-1-5225-8535-0.ch002
Kollanyi... | Social_Media_and_Democracy |
Batch size. [Chen et al., 2021b] found that large batch (e.g., 4096) training for joint-
embedding ViT SSL methods can be unstable. This instability does not re๏ฌect as a large
drop in the ๏ฌnal accuracy, but appears as drops in kNN probe accuracy during training
when the Lโ โ norm of the gradient spikes. Using a random ... | A Cookbook of Self-Supervised Learning |
Tractable Regularization of Probabilistic Circuits
Department of Computer Science
Anji Liu
UCLA
Los Angeles, CA 90095
liuanji@cs.ucla.edu
Guy Van den Broeck
Department of Computer Science
UCLA
Los Angeles, CA 90095
guyvdb@cs.ucla.edu
Abstract | Tractable Regularization of Probabilistic Circuits |
Figure 2: Inception Prompt of AI Society Role-Playing. This shows the task speci๏ฌer prompt,
assistant system prompt, and user system prompt which are used for studying the AI society scenario.
The prompts used for the Code scenario follow a similar sprint as the AI society scenario, but with
some additional engineerin... | CAMEL- Communicative Agents for โMindโ Exploration of Large Scale Language Model Society |
To perform the task, we estimate optical ๏ฌow from
RAFT [59] and produce monocular depth maps from MI-
DAS [48], and then normalize and concatenate on the
channel dimension. This conveniently produces the same
number of channels as the RGB ground truth and so can
be tokenized in the same fashion as RGB videos with
the M... | VideoPoet |
Finally, of course, billions of individual users are embracing digital media,
not only to get news but also to express themselves, connect, and build
communities. In the next section, we examine their aggregate individual-level
choices, but it is important to recognize that these choices also have an informal,
institut... | Social_Media_and_Democracy |
Game worlds that fragment lose the ability to incrementally expand.
Open Economies
In-game economies are another dimension with almost limitless potential for
player creativity. Weโll use EVE, the | The Open Problems of Onchain Games |
3. Information Integration
This ability assesses whether the model can integrate
information from multiple documents to answer more
complex questions.
4. Counterfactual Robustness
This test aims to evaluate whether the model can iden-
tify and deal with known erroneous information in doc-
uments when receiving instr... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
given human instructions; (3) dialogue-based image drawing and editing: to enable understanding and
generating images, Visual ChatGPT (Wu et al., 2023) opts to interleave various vision foundation models
with ChatGPT. In their system, ChatGPT serves as the core controller and makes sequential decisions. At
each step, C... | Tool Learning with Foundation Models |
1. Drafts an initial response.
2. Plans verification questions to fact-check its
draft.
3. Answers those questions independently so the
answers are unbiased.
4. Generates a final verified response. | AComprehensiveSurveyofHallucinationMitigationTechniquesinLarge LanguageModels |
would not require changing section 230 but could be legislated independently.
Hwang warns about changing the intermediary liability rules in section 230.
Like Keller and Leerssen, he worries that platforms might overcorrect, take
down more speech than required, and become less transparent. | Social_Media_and_Democracy |
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A Smith, Daniel Khashabi, and Hannaneh
Hajishirzi. Self-instruct: Aligning language model with self generated instructions. ArXiv preprint,
abs/2212.10560, 2022c. URL https://arxiv.org/abs/2212.10560.
Sherwood L Washburn. Tools and human evolution. Scienti๏ฌc... | Tool Learning with Foundation Models |
5https://github.com/LAION-AI/CLAP
6https://github.com/gudgud96/
frechet-audio-distance
ElectronicHip HopMetalPopElectronicHip HopMetalPop051015202530ElectronicHip HopMetalPopElectronicHip HopMetalPop051015202530 ation of FAD, our model has the best score, which
is one magnitude smaller than previous models.
Moreover... | Mouฬsai |
Transformers have been successfully applied in end-to-end speech processing, including auto-
matic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS) [309]. In 2018,
the Speech-Transformer was introduced as a no-recurrence sequence-to-sequence model for speech
recognition. To reduce the dimensi... | AReviewofDeepLearningTechniquesforSpeechProcessing |
7 DISCUSSION
Conclusion. Modeling 3D humans accurately and robustly
from a single RGB image is an extremely ill-posed problem
due to the varieties of body poses, clothing types, view
points and other environment factors. Our key idea to over-
come these challenges is factoring out pose estimation from
surface reconstru... | PaMIR- Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction |
our ablations. We train on 30-second audio crops sampled at random from the full track. We train
the models for 1M steps with the AdamW optimizer [Loshchilov and Hutter, 2017], a batch size of
192 examples, ฮฒ1 = 0.9, ฮฒ2 = 0.95, a decoupled weight decay of 0.1 and gradient clipping of 1.0.
We further rely on D-Adaptatio... | Simple and Controllable Music Generation |
In a busy city street, a pedestrian surrounded by distrac-
tions can pick out a single sign if it is relevant to their route.
Artificial agents in outdoor Vision-and-Language Naviga-
tion (VLN) are also confronted with detecting supervisory
signal on environment features and location in inputs. To
boost the prominence ... | APriorityMapforVision-and-LanguageNavigation withTrajectoryPlansandFeature-LocationCues |
t
u
r
a
l
L
a
n
g
u
a
g
e
P
r
o
c
e
s
s
i
n
g
S
e
c
t
i
o
n
p
r
o
v
i
d
e
s
a
s
t
r
o
n
g
,
i
n
t
e
r
n
a
t
i
o
n
a
l
a
n
d
d
i
v
e
r
s
e
e
n
v
i
r
o
n
m
e
n
t
f
o
r
r
e
s
e
a
r
c
h
w
i
t
h
i
n
c
o
r
e
a
s
w
e
l
l
a
s
e
m
e
r
g
i
n
g
t
o
p
i
c
s
i
n
n
a
t
u
r
a
l
l
a
n
g
u
a
g
... | PhD Fellow in Explainable Natural Language Understanding |
BloombergGPT, though remarkable in its finance-specific
capabilities, comes with an intensive computational require-
ment. It used approximately 1.3 million GPU hours for train-
ing, which, when calculated using AWS cloudโs $2.3 rate,
translates to a staggering cost of around $3 million per train-
ing. In contrast to t... | FinGPT-Open-SourceFinancialLargeLanguageModels |
h
a
r
e
o
n
a
r
e
s
u
m
e
o
r
L
i
n
k
e
d
I
n
โ
a
n
d
u
s
e
t
h
i
s
d
a
t
a
t
o
n
o
t
j
u
s
t
| Product-Led AI _ Greylock |
learning rate and lower momentum may be more suitable.
11. For tasks with simpler prediction tasks, a lower initial learning
rate and higher momentum may be more suitable.
Test task: MNIST, Max Pooling CNN with Tanh
1. Set the initial learning rate to a high value to ensure that the
model is able to learn quickly ... | MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks |
Chess, S., & Shaw, A. (2015). A conspiracy of ๏ฌshes, or, how we learned to stop
worrying about# GamerGate and embrace hegemonic masculinity. Journal of
Broadcasting and Electronic Media, 59(1), 208โ220.
Chetty, N., & Alathur, S. (2018). Hate speech review in the context of online social
networks. Aggression and Viole... | Social_Media_and_Democracy |
15
๐ฃ(๐)๐(๐)๐(๐โฒ)๐ (๐,๐โฒ)It ~ Tt!~ TXZY๐(โ)๐ฃ(๐โฒ)XโฒZโฒYโฒ๐โฒ(+โโฒ)+๐ฃ๐๐ Tt,tโ๐!,๐โฒ!"โ#,โโฒ#"IX, Xโ Y, Yโ Z, Zโ: maintain variance: bring covariance to zero: minimize distance: distribution oftransformations: random transformations: encoders: expanders: batch of images: batches of views: batches of representati... | A Cookbook of Self-Supervised Learning |
3
minimize the negative marginal log-likelihood of each target,(cid:80)
j โ log p(yj|xj) using stochastic
gradient descent with Adam [28]. Updating the document encoder BERTd during training is costly as
it requires the document index to be periodically updated as REALM does during pre-training [20].
We do not ๏ฌnd t... | Retrieval-AugmentedGenerationfor Knowledge-IntensiveNLPTasks |
2022), and computer programming (Chen et al., 2021; Xu
et al., 2022; Fried et al., 2022). Despite these successes,
very little is known about how and why these models are so
successful.
Critical to understanding the functioning of transformers
is better understanding how these models behave along
two axes: training and... | Pythia- A Suite for Analyzing Large Language Models Across Training and Scaling |
8
(c) Sequential models(normal model โcolor model)Input images(d) Sequential models(color model โnormal model)(a) Cross-domain modelw/ cross-domain attention(b) Cross-domain modelw/o cross-domain attention Figure 8. Ablation study on the strategies in the mesh extraction module: geometry-aware normal loss and outlier-... | Wonder3D |
three columns of Fig. 5, CLIP-
Mesh, SJC, and DreamFusion struggle to generate complex
3D scenes related to the given prompts since their primary
design focus on simple 3D object generation. Consequently,
their BRISQUE and NIQE values tend to be higher compared
to other methods, indicating relatively poorer quality in ... | Text2NeRF- Text-Driven 3D Scene Generation with Neural Radiance Fields |
Similarly, we advocate for a more thorough anal-
ysis of the task data itself, including, for example,
the quality of the test data (e.g., number of exam-
ples), possible data leaks, and the specific abilities
required for solving them (e.g., formal linguistic
abilities, functional linguistic abilities, or memory),
muc... | AreEmergentAbilitiesinLarge Language Models just In-Context |
In fact, The size of SOTA language model increases by at least a factor of 10 every year:
BERT-Large (2018) has 355M parameters, GPT-2 (early 2019) reaches 1.5B, T5 (late 2019)
further streches to 11B, GPT-3 (mid-2020) finally gets to 175B. The progress of the sizes of
language models clearly outpace the growth of GPU ... | OpenAI's GPT-3 Language Model_ A Technical Overview |
the other hand, is signi๏ฌcantly less detailed. It only documents the number of
unique accounts reported and actioned for six different categories of violations,
without specifying appeal or reinstatement rates or reporting mechanisms other
than those from known government entities (Twitter 2018). | Social_Media_and_Democracy |
Each pair of trait adjectives is associated with low and high levels of a specific
component of the Big Five. To achieve more precise control of personality levels, we
hypothesize that the linguistic qualifiers often used in Likert-type response scales [65]
(e.g., โa bit,โ โvery,โ โextremelyโ) are useful for setting up... | PersonalityTraitsinLargeLanguageModels |
architecture used in original DQN paper (Mnih et al., 2015). We apply sparsity to the existing model
using the ERK distributions and at 98% target sparsity. We ran our experiments for 40M frames, 5
independent seeds and report the average returns calculated over 125000 environment steps at the
end of the training. | JAXPRUNER |
5.3 DEBERTA XXL
DeBERTa (He et al., 2021) is a more recent variant of BERT that is trained on a much larger
scale and performs very competitively on benchmarks such as GLUE (Wang et al., 2019) and Su-
perGLUE (Wang et al., 2020). We evaluate if LoRA can still match the performance of a fully
๏ฌne-tuned DeBERTa XXL (1.5... | LORA |
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. Commonsenseqa: A question
answering challenge targeting commonsense knowledge. In Jill Burstein, Christy Doran, and
Thamar Solorio, editors, Proceedings of the 2019 Conference of the North American Chapter of the
Association for Computational Linguisti... | Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models |
for better option pricing.
volume 13. MIT Press, 2001. 4
[21] Yao Feng, Haiwen Feng, Michael J. Black, and Timo Bolkart. Learning an animatable detailed 3D face model from in-the-wild
images. ACM Transactions on Graphics (ToG), Proc. SIGGRAPH, 40(4):88:1โ88:13, Aug. 2021. 1, 2, 6, 5
[22] Guy Gafni, Justus Thies, Mic... | I M Avatar- Implicit Morphable Head Avatars from Videos |
41 Hansard. 2017. HL Deb 787 Col. 1261. http://bit.ly/2kctmPL
42 European Parliament. Legislative resolution of 17 April 2019 on the proposal for a regulation of
the European Parliament and of the Council on preventing the dissemination of terrorist content
online (provisional edition), P8_TA-PROV(2019)0421. www.europa... | Social_Media_and_Democracy |
The development of RAG algorithms and models is il-
lustrated in Fig 1. On a timeline, most of the research re-
lated to RAG emerged after 2020, with a significant turn-
ing point in December 2022 when ChatGPT was released.
Since the release of ChatGPT, research in the field of natu-
ral language processing has entered... | Retrieval-AugmentedGenerationforLargeLanguageModels-ASurvey |
Liang, P., Bommasani, R., Lee, T., Tsipras, D., Soylu, D., Yasunaga, M., Zhang, Y., Narayanan, D., Wu, Y., Kumar,
A., Newman, B., Yuan, B., Yan, B., Zhang, C., Cosgrove, C., Manning, C. D., Rรฉ, C., Acosta-Navas, D., Hudson,
D. A., Zelikman, E., Durmus, E., Ladhak, F., Rong, F., Ren, H., Yao, H., Wang, J., Santhanam, K.... | PaLM 2 Technical Report |
cross-lingual data [Li et al., 2023b]. Retrieval units vary from
tokens (e.g., kNN-LM [Khandelwal et al., 2019]) to phrases
(e.g., NPM, COG [Lee et al., 2020, Lan et al., 2022]) and
document paragraphs, with finer granularities offering pre-
cision at the cost of increased retrieval complexity. | RAG forLargeLanguageModels-ASurvey |
[Berchansky et al., 2023] Moshe Berchansky, Peter Izsak,
Avi Caciularu, Ido Dagan, and Moshe Wasserblat. Opti-
mizing retrieval-augmented reader models via token elim-
ination. arXiv preprint arXiv:2310.13682, 2023.
[Blagojevi, 2023] Vladimir Blagojevi.
pipelines in haystack:
lostinthemiddleranker.
enhancing-rag-pipe... | RAG forLargeLanguageModels-ASurvey |
PIFuโ
PaMIRโ
Training set scale
P2S โ
P2S โ
Chamfer โ
Chamfer โ
Chamfer โ
ICON
P2S โ
1/8x
3.339
3.280
2.024
1.791
1.336
1.286
1/4x
2.968
2.859
1.780
1.778
1.266
1.235
1/2x
2.932
2.812
1.479
1.662
1.219
1.184
1x
2.682
2.658
1.350
1.283
1.142
1.065
8x
1.760
1.547
1.095
1.131
1.036
1.063
Table 9. Reconstructio... | ICON |
search. arXiv preprint arXiv:1806.03198, 2018. 13
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved
techniques for training gans. Advances in neural information processing systems, 29,
2016. 6
M. B. Sariyildiz, Y. Kalantidis, K. Alahari, and D. Larlus. Improving the generalization o... | A Cookbook of Self-Supervised Learning |
sk, ak+1, sk+1 in G1 such that t โ f (sk+1), i.e. t[V C] = sk+1[V C]. There must exist some state t
that is, t
(cid:10)[V C] (cid:4) post(g(ak+1))[V C] = t[V C] and
(a) t
(cid:10)[V \ V C] (cid:4) post(g(ak+1))[V \ V C] = t[V \ V C].
(b) t
(cid:10)[V C] = sk[V C], since post(g(ak+1)) = post(ak+1)) and t[V C] = sk+1[... | A-framework-for-analysing-state-abstraction-metho_2022_Artificial-Intelligen |
dependencyHybrid Endpoints[T1] [T3] [T5] [T2] [T4] [T6] Demonstration-based Parsing HuggingGPT introduces in-context learning for more effective | HuggingGPT- Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
We evaluate pre๏ฌx-tuning on table-to-text gen-
eration using GPT-2 and abstractive summariza-
tion using BART. In terms of storage, pre๏ฌx-tuning
stores 1000x fewer parameters than full ๏ฌne-tuning.
In terms of performance when trained on full
datasets, pre๏ฌx-tuning and ๏ฌne-tuning are compara-
ble for table-to-text (ยง6.1... | Prefix-Tuning |
Chapter 6, by Wesleyan professor Erika Franklin Fowler, Bowdoin College
professor Michael M. Franz, and Washington State professor Travis N. Ridout,
covers political advertising. It pays particular attention to the United States,
since it is responsible for more political advertising than any other country in the
world... | Social_Media_and_Democracy |
43
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothรฉe Lacroix,
Baptiste Roziรจre, Naman Goyal, Eric Hambro, Faisal Azhar, Aurโelien Rodriguez, Armand Joulin, Edouard
Grave, and Guillaume Lample. Llama: Open and efficient foundation language models. arXiv preprint
arXiv:2302.13... | Llama2 |
Another way in which this orientation manifests is print media, by way of
contrast with France. On the one hand, both France and Germany have
traditionally had vibrant local and regional newspaper markets. As recently as
2013, for example, half of all newspapers sold in Germany were regional papers
(Stelzig 2015, p. 71... | Social_Media_and_Democracy |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.